------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ log: /Users/dguz/Projects/svn/CO/MSE/casanares/desp-denuncias/output/logs/estimaciones.log log type: text opened on: 21 Nov 2007, 12:13:21 . insheet using ../output/data/matched-exploracion.csv, clear (141 vars, 2138 obs) . . . estimacion ong gov for _todo ong gov for cell values: 1 28 33 64 4 202 143 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -66.063036 Iteration 1: log likelihood = -51.893271 Iteration 2: log likelihood = -51.86938 Iteration 3: log likelihood = -51.869379 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 68.00931533 (1/df) Deviance = 22.66977 Pearson = 62.57592101 (1/df) Pearson = 20.85864 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -51.86937906 AIC = 15.96268 BIC = 62.17158488 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.280346 .1451711 -15.71 0.000 -2.564876 -1.995816 x2 | -1.56443 .1413526 -11.07 0.000 -1.841476 -1.287384 x3 | -1.872472 .1409949 -13.28 0.000 -2.148817 -1.596127 _cons | 6.783651 .1511945 44.87 0.000 6.487315 7.079987 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -63.409998 Iteration 1: log likelihood = -49.772819 Iteration 2: log likelihood = -49.698022 Iteration 3: log likelihood = -49.698016 Iteration 4: log likelihood = -49.698016 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 63.66658945 (1/df) Deviance = 31.83329 Pearson = 68.95964577 (1/df) Pearson = 34.47982 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -49.69801612 AIC = 15.628 BIC = 59.77476915 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.555682 .2015094 -12.68 0.000 -2.950634 -2.160731 x2 | -1.802517 .1874863 -9.61 0.000 -2.169984 -1.435051 x3 | -2.045994 .1723866 -11.87 0.000 -2.383865 -1.708122 x12 | .5951021 .2827422 2.10 0.035 .0409376 1.149267 _cons | 7.008838 .1915989 36.58 0.000 6.633311 7.384365 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -50.981347 Iteration 1: log likelihood = -39.252765 Iteration 2: log likelihood = -39.105013 Iteration 3: log likelihood = -39.104986 Iteration 4: log likelihood = -39.104986 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 42.48052899 (1/df) Deviance = 21.24026 Pearson = 43.95501579 (1/df) Pearson = 21.97751 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -39.10498589 AIC = 12.60142 BIC = 38.58870869 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.899443 .2060585 -14.07 0.000 -3.303311 -2.495576 x2 | -1.984131 .1856602 -10.69 0.000 -2.348019 -1.620244 x3 | -2.430799 .1959419 -12.41 0.000 -2.814838 -2.04676 x13 | 1.435371 .2804899 5.12 0.000 .8856212 1.985121 _cons | 7.292399 .1985452 36.73 0.000 6.903258 7.68154 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -42.985839 Iteration 1: log likelihood = -20.759069 Iteration 2: log likelihood = -19.595791 Iteration 3: log likelihood = -19.582002 Iteration 4: log likelihood = -19.581996 Iteration 5: log likelihood = -19.581996 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 3.434549911 (1/df) Deviance = 1.717275 Pearson = 3.461464357 (1/df) Pearson = 1.730732 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.58199635 AIC = 7.023428 BIC = -.4572703876 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.727938 .13782 -12.54 0.000 -1.99806 -1.457815 x2 | -.6122626 .1834905 -3.34 0.001 -.9718974 -.2526278 x3 | -.8798579 .1870902 -4.70 0.000 -1.246548 -.5131678 x23 | -2.948783 .4892347 -6.03 0.000 -3.907666 -1.989901 _cons | 5.886821 .1860628 31.64 0.000 5.522144 6.251497 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -43.351832 Iteration 1: log likelihood = -19.086714 Iteration 2: log likelihood = -17.996719 Iteration 3: log likelihood = -17.984803 Iteration 4: log likelihood = -17.984795 Iteration 5: log likelihood = -17.984795 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .2401479471 (1/df) Deviance = .2401479 Pearson = .2770702569 (1/df) Pearson = .2770703 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.98479537 AIC = 6.852799 BIC = -1.705762202 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.466337 .1931218 -7.59 0.000 -1.844849 -1.087825 x2 | -.3188502 .2405575 -1.33 0.185 -.7903342 .1526339 x3 | -.6623755 .2143083 -3.09 0.002 -1.082412 -.2423391 x12 | -.4942433 .276827 -1.79 0.074 -1.036814 .0483276 x23 | -3.166266 .5002758 -6.33 0.000 -4.146789 -2.185743 _cons | 5.62522 .2300457 24.45 0.000 5.174339 6.076101 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -35.432506 Iteration 1: log likelihood = -25.146852 Iteration 2: log likelihood = -24.795173 Iteration 3: log likelihood = -24.791884 Iteration 4: log likelihood = -24.791884 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 13.85432503 (1/df) Deviance = 13.85433 Pearson = 10.59085375 (1/df) Pearson = 10.59085 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -24.79188391 AIC = 8.797681 BIC = 11.90841488 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.6246 .5245816 -8.82 0.000 -5.652761 -3.596439 x2 | -3.57655 .5069448 -7.06 0.000 -4.570144 -2.582957 x3 | -3.921973 .5049262 -7.77 0.000 -4.911611 -2.932336 x12 | 2.369135 .5493497 4.31 0.000 1.29243 3.445841 x13 | 2.926545 .5433524 5.39 0.000 1.861594 3.991496 _cons | 8.884818 .5118042 17.36 0.000 7.8817 9.887936 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -39.232408 Iteration 1: log likelihood = -19.389936 Iteration 2: log likelihood = -17.874467 Iteration 3: log likelihood = -17.867174 Iteration 4: log likelihood = -17.867172 Iteration 5: log likelihood = -17.867172 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .0049019537 (1/df) Deviance = .004902 Pearson = .004979492 (1/df) Pearson = .0049795 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.86717237 AIC = 6.819192 BIC = -1.941008195 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.976063 .2016551 -9.80 0.000 -2.3713 -1.580826 x2 | -.8266786 .2265817 -3.65 0.000 -1.270771 -.3825865 x3 | -1.172527 .2514944 -4.66 0.000 -1.665447 -.6796067 x13 | .5119911 .2772711 1.85 0.065 -.0314503 1.055433 x23 | -2.734368 .5069725 -5.39 0.000 -3.728015 -1.74072 _cons | 6.134946 .2372547 25.86 0.000 5.669936 6.599957 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 883.2879 .0228598 62.17159 62.57592 68.00932 3 1.65e-13 | 2. | 2 1106.368 .0367102 59.77477 68.95965 63.66659 2 1.06e-15 | 3. | 3 1469.091 .0394202 38.58871 43.95502 42.48053 2 2.85e-10 | 4. | 4 360.2581 .0346194 -.4572704 3.461464 3.43455 2 .1771547 | 5. | 5 277.3333 .052921 -1.705762 .2770703 .2401479 1 .5986279 | |-------------------------------------------------------------------------------| 6. | 6 7221.5 .2619435 11.90841 10.59085 13.85433 1 .0011365 | 7. | 7 461.7143 .0562898 -1.941008 .0049795 .004902 1 .9437435 | +-------------------------------------------------------------------------------+ nk is 475 Mal modelo: Sistema 4to solo > estimacion (1069 > 682) (note: file /tmp/model_info_ong_gov_for_todo.dta not found) (note: file /tmp/bma_info_ong_gov_for_todo.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_todo.dta not found) . estimacion ong gov jud _todo ong gov jud cell values: 19 10 51 46 110 96 1192 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -140.71512 Iteration 1: log likelihood = -33.757415 Iteration 2: log likelihood = -32.723782 Iteration 3: log likelihood = -32.721013 Iteration 4: log likelihood = -32.721013 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 23.18546497 (1/df) Deviance = 7.728488 Pearson = 30.76642613 (1/df) Pearson = 10.25548 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -32.72101316 AIC = 10.49172 BIC = 17.34773453 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.906794 .1020986 -28.47 0.000 -3.106904 -2.706684 x2 | -2.235059 .0835067 -26.77 0.000 -2.398729 -2.071389 x3 | .2584223 .106759 2.42 0.015 .0491785 .4676661 _cons | 6.810752 .1102418 61.78 0.000 6.594682 7.026822 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -80.923723 Iteration 1: log likelihood = -24.264182 Iteration 2: log likelihood = -21.982681 Iteration 3: log likelihood = -21.971055 Iteration 4: log likelihood = -21.971053 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.685544512 (1/df) Deviance = .8427723 Pearson = 1.652600431 (1/df) Pearson = .8263002 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.97105293 AIC = 7.706015 BIC = -2.206275786 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.120855 .1170116 -26.67 0.000 -3.350193 -2.891516 x2 | -2.36769 .0907568 -26.09 0.000 -2.54557 -2.18981 x3 | .1690763 .1101567 1.53 0.125 -.0468269 .3849795 x12 | 1.160275 .2302799 5.04 0.000 .7089344 1.611615 _cons | 6.914312 .113901 60.70 0.000 6.69107 7.137553 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -142.28899 Iteration 1: log likelihood = -33.721742 Iteration 2: log likelihood = -32.693377 Iteration 3: log likelihood = -32.691099 Iteration 4: log likelihood = -32.691099 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 23.12563669 (1/df) Deviance = 11.56282 Pearson = 30.79916468 (1/df) Pearson = 15.39958 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -32.69109902 AIC = 10.76889 BIC = 19.23381639 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.868553 .1864612 -15.38 0.000 -3.23401 -2.503096 x2 | -2.227148 .0892751 -24.95 0.000 -2.402124 -2.052172 x3 | .2777524 .1329612 2.09 0.037 .0171533 .5383516 x13 | -.0546089 .2232074 -0.24 0.807 -.4920873 .3828696 _cons | 6.791496 .1355976 50.09 0.000 6.52573 7.057263 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -136.61803 Iteration 1: log likelihood = -34.44706 Iteration 2: log likelihood = -32.394374 Iteration 3: log likelihood = -32.391167 Iteration 4: log likelihood = -32.391167 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 22.52577219 (1/df) Deviance = 11.26289 Pearson = 29.38013839 (1/df) Pearson = 14.69007 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -32.39116677 AIC = 10.68319 BIC = 18.63395189 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.860771 .1149579 -24.89 0.000 -3.086085 -2.635458 x2 | -2.081621 .207356 -10.04 0.000 -2.488031 -1.675211 x3 | .3802232 .1853835 2.05 0.040 .0168782 .7435682 x23 | -.1838499 .2270527 -0.81 0.418 -.6288651 .2611653 _cons | 6.689413 .1869611 35.78 0.000 6.322976 7.05585 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -80.850229 Iteration 1: log likelihood = -24.194859 Iteration 2: log likelihood = -21.908618 Iteration 3: log likelihood = -21.896923 Iteration 4: log likelihood = -21.896921 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.537279995 (1/df) Deviance = 1.53728 Pearson = 1.507940392 (1/df) Pearson = 1.50794 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.89692067 AIC = 7.970549 BIC = -.4086301545 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.151562 .1429922 -22.04 0.000 -3.431822 -2.871303 x2 | -2.448474 .2285146 -10.71 0.000 -2.896354 -2.000594 x3 | .1031842 .2033396 0.51 0.612 -.295354 .5017224 x12 | 1.190982 .2445075 4.87 0.000 .7117561 1.670208 x23 | .0931891 .2419357 0.39 0.700 -.3809961 .5673743 _cons | 6.980204 .2053921 33.98 0.000 6.577643 7.382765 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -80.924158 Iteration 1: log likelihood = -24.221992 Iteration 2: log likelihood = -21.909609 Iteration 3: log likelihood = -21.8977 Iteration 4: log likelihood = -21.897698 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.538835417 (1/df) Deviance = 1.538835 Pearson = 1.513970766 (1/df) Pearson = 1.513971 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.89769839 AIC = 7.970771 BIC = -.4070747323 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.183475 .2014359 -15.80 0.000 -3.578282 -2.788668 x2 | -2.382907 .0996486 -23.91 0.000 -2.578215 -2.1876 x3 | .1361322 .1396695 0.97 0.330 -.1376151 .4098794 x12 | 1.175492 .2339271 5.03 0.000 .7170038 1.633981 x13 | .0870114 .2272673 0.38 0.702 -.3584244 .5324471 _cons | 6.947256 .1426412 48.70 0.000 6.667684 7.226827 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -113.57079 Iteration 1: log likelihood = -32.623632 Iteration 2: log likelihood = -30.891618 Iteration 3: log likelihood = -30.874391 Iteration 4: log likelihood = -30.874387 Iteration 5: log likelihood = -30.874387 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 19.4922127 (1/df) Deviance = 19.49221 Pearson = 27.2534493 (1/df) Pearson = 27.25345 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -30.87438703 AIC = 10.53554 BIC = 17.54630255 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.261763 .33229 -6.81 0.000 -2.91304 -1.610487 x2 | -1.526056 .3489114 -4.37 0.000 -2.20991 -.8422027 x3 | .9825106 .364691 2.69 0.007 .2677294 1.697292 x13 | -.6613984 .3542181 -1.87 0.062 -1.355653 .0328562 x23 | -.7394143 .3609648 -2.05 0.041 -1.446892 -.0319364 _cons | 6.090405 .3635324 16.75 0.000 5.377894 6.802915 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 907.5532 .0121532 17.34773 30.76643 23.18546 3 9.52e-07 | 2. | 2 1006.578 .0129734 -2.206276 1.6526 1.685544 2 .4376656 | 3. | 3 890.2446 .0183867 19.23382 30.79916 23.12564 2 2.05e-07 | 4. | 4 803.85 .0349544 18.63395 29.38014 22.52577 2 4.17e-07 | 5. | 5 1075.137 .0421859 -.4086302 1.50794 1.53728 1 .2194536 | |-------------------------------------------------------------------------------| 6. | 6 1040.291 .0203465 -.4070747 1.513971 1.538835 1 .2185342 | 7. | 7 441.6 .1321558 17.5463 27.25345 19.49221 1 1.78e-07 | +-------------------------------------------------------------------------------+ nk is 1524 Buen modelo: Sistema 4to solo < estimacion (20 < 1007) BICs = -2.21 -0.41 -0.41 Estimacion subregistro 1007 IC ( 774 ; 1240) Estimacion del total 2531 IC ( 2298 ; 2764) Estimacion BMA: 1029 IC ( 715 ; 1343) Total registros = 1524 (note: file /tmp/model_info_ong_gov_jud_todo.dta not found) (note: file /tmp/bma_info_ong_gov_jud_todo.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_todo.dta not found) . estimacion ong for jud _todo ong for jud cell values: 33 1 37 55 127 20 1175 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -191.20546 Iteration 1: log likelihood = -74.140392 Iteration 2: log likelihood = -72.992201 Iteration 3: log likelihood = -72.98881 Iteration 4: log likelihood = -72.98881 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 106.9042845 (1/df) Deviance = 35.63476 Pearson = 125.8283758 (1/df) Pearson = 41.94279 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -72.98881009 AIC = 21.9968 BIC = 101.0665541 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.615458 .0981959 -26.64 0.000 -2.807918 -2.422997 x2 | -2.220798 .0855884 -25.95 0.000 -2.388548 -2.053048 x3 | 1.056807 .1323531 7.98 0.000 .7973993 1.316214 _cons | 5.993606 .1352615 44.31 0.000 5.728499 6.258714 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -120.6048 Iteration 1: log likelihood = -58.008872 Iteration 2: log likelihood = -56.024618 Iteration 3: log likelihood = -56.019646 Iteration 4: log likelihood = -56.019645 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 72.96595483 (1/df) Deviance = 36.48298 Pearson = 72.04715149 (1/df) Pearson = 36.02358 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -56.01964525 AIC = 17.43418 BIC = 69.07413453 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.873503 .1146027 -25.07 0.000 -3.09812 -2.648886 x2 | -2.404859 .0952204 -25.26 0.000 -2.591487 -2.21823 x3 | .9524704 .1350335 7.05 0.000 .6878097 1.217131 x12 | 1.409431 .2221447 6.34 0.000 .9740353 1.844826 _cons | 6.116553 .1381488 44.28 0.000 5.845786 6.38732 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -158.79894 Iteration 1: log likelihood = -53.434229 Iteration 2: log likelihood = -52.897055 Iteration 3: log likelihood = -52.89649 Iteration 4: log likelihood = -52.89649 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 66.71964531 (1/df) Deviance = 33.35982 Pearson = 98.07604672 (1/df) Pearson = 49.03802 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -52.89649049 AIC = 16.54185 BIC = 62.82782501 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.153006 .2708654 -4.26 0.000 -1.683893 -.62212 x2 | -2.063003 .0836687 -24.66 0.000 -2.22699 -1.899015 x3 | 1.993299 .2372318 8.40 0.000 1.528333 2.458265 x13 | -1.770155 .297358 -5.95 0.000 -2.352966 -1.187344 _cons | 5.058735 .2387477 21.19 0.000 4.590798 5.526672 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -198.16451 Iteration 1: log likelihood = -52.062925 Iteration 2: log likelihood = -49.07731 Iteration 3: log likelihood = -49.051646 Iteration 4: log likelihood = -49.051642 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 59.02994923 (1/df) Deviance = 29.51497 Pearson = 90.23120246 (1/df) Pearson = 45.1156 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -49.05164245 AIC = 15.44333 BIC = 55.13812893 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.924221 .1218234 -24.00 0.000 -3.162991 -2.685452 x2 | -3.939346 .2813672 -14.00 0.000 -4.490815 -3.387876 x3 | .1161589 .1799265 0.65 0.519 -.2364905 .4688083 x23 | 1.914492 .2936709 6.52 0.000 1.338908 2.490077 _cons | 6.931554 .1817216 38.14 0.000 6.575386 7.287722 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -101.16781 Iteration 1: log likelihood = -26.032002 Iteration 2: log likelihood = -21.57781 Iteration 3: log likelihood = -21.523169 Iteration 4: log likelihood = -21.523156 Iteration 5: log likelihood = -21.523156 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 3.972975832 (1/df) Deviance = 3.972976 Pearson = 3.061945762 (1/df) Pearson = 3.061946 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.52315575 AIC = 7.863759 BIC = 2.027065683 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.458106 .1669673 -20.71 0.000 -3.785355 -3.130856 x2 | -4.628981 .3081506 -15.02 0.000 -5.232945 -4.025017 x3 | -.3964153 .2126237 -1.86 0.062 -.8131501 .0203196 x12 | 1.994033 .2531651 7.88 0.000 1.497839 2.490228 x23 | 2.427067 .3147664 7.71 0.000 1.810136 3.043998 _cons | 7.465439 .2146157 34.79 0.000 7.0448 7.886078 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -103.50216 Iteration 1: log likelihood = -41.395522 Iteration 2: log likelihood = -39.587976 Iteration 3: log likelihood = -39.585057 Iteration 4: log likelihood = -39.585057 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 40.09677842 (1/df) Deviance = 40.09678 Pearson = 32.48487847 (1/df) Pearson = 32.48488 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -39.58505705 AIC = 13.0243 BIC = 38.15086827 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.50971 .2819845 -5.35 0.000 -2.06239 -.9570307 x2 | -2.224836 .0934081 -23.82 0.000 -2.407913 -2.04176 x3 | 1.848455 .2405702 7.68 0.000 1.376946 2.319964 x12 | 1.229408 .2213739 5.55 0.000 .7955234 1.663293 x13 | -1.625311 .3000281 -5.42 0.000 -2.213356 -1.037267 _cons | 5.220569 .2423326 21.54 0.000 4.745605 5.695532 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -147.97206 Iteration 1: log likelihood = -56.486836 Iteration 2: log likelihood = -49.776023 Iteration 3: log likelihood = -49.062205 Iteration 4: log likelihood = -49.049129 Iteration 5: log likelihood = -49.049117 Iteration 6: log likelihood = -49.049117 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 59.02489779 (1/df) Deviance = 59.0249 Pearson = 90.14044625 (1/df) Pearson = 90.14045 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -49.04911673 AIC = 15.72832 BIC = 57.07898764 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.995732 1.024695 -2.92 0.003 -5.004098 -.9873668 x2 | -4.007333 1.00905 -3.97 0.000 -5.985035 -2.029632 x3 | .0445937 1.033947 0.04 0.966 -1.981905 2.071092 x13 | .0725707 1.032014 0.07 0.944 -1.95014 2.095282 x23 | 1.98248 1.01255 1.96 0.050 -.0020811 3.967041 _cons | 7.003065 1.033529 6.78 0.000 4.977386 9.028745 ------------------------------------------------------------------------------ +------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |------------------------------------------------------------------------------| 1. | 1 400.8577 .0182957 101.0666 125.8284 106.9043 3 4.29e-27 | 2. | 2 453.2995 .0190851 69.07413 72.04715 72.96596 2 2.27e-16 | 3. | 3 157.3913 .0570004 62.82782 98.07605 66.71964 2 5.05e-22 | 4. | 4 1024.084 .0330228 55.13813 90.2312 59.02995 2 2.55e-20 | 5. | 5 1746.622 .0460599 2.027066 3.061946 3.972976 1 .0801456 | |------------------------------------------------------------------------------| 6. | 6 185.0394 .0587251 38.15087 32.48488 40.09678 1 1.20e-08 | 7. | 7 1100 1.068182 57.07899 90.14045 59.0249 1 2.22e-21 | +------------------------------------------------------------------------------+ nk is 1448 Buen modelo: Sistema 4to solo < estimacion (96 < 1747) BICs = 2.03 38.15 55.14 Estimacion subregistro 1747 IC ( 1008 ; 2486) Estimacion del total 3195 IC ( 2456 ; 3934) Estimacion BMA: 1747 IC ( 1008 ; 2486) Total registros = 1448 (note: file /tmp/model_info_ong_for_jud_todo.dta not found) (note: file /tmp/bma_info_ong_for_jud_todo.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_todo.dta not found) . estimacion gov for jud _todo gov for jud cell values: 5 0 124 106 155 21 1088 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -128.83083 Iteration 1: log likelihood = -60.252219 Iteration 2: log likelihood = -60.166814 Iteration 3: log likelihood = -60.166804 Iteration 4: log likelihood = -60.166804 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 83.08777245 (1/df) Deviance = 27.69592 Pearson = 74.12610414 (1/df) Pearson = 24.7087 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -60.16680378 AIC = 18.33337 BIC = 77.250042 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.028845 .0775926 -26.15 0.000 -2.180924 -1.876767 x2 | -2.319701 .0848767 -27.33 0.000 -2.486056 -2.153345 x3 | .7466747 .1066579 7.00 0.000 .537629 .9557205 _cons | 6.260051 .1102567 56.78 0.000 6.043952 6.47615 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -136.08998 Iteration 1: log likelihood = -61.289618 Iteration 2: log likelihood = -50.472293 Iteration 3: log likelihood = -49.693498 Iteration 4: log likelihood = -49.686186 Iteration 5: log likelihood = -49.686183 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 62.12653157 (1/df) Deviance = 31.06327 Pearson = 56.73731035 (1/df) Pearson = 28.36866 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -49.68618334 AIC = 15.62462 BIC = 58.23471127 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.923636 .0797181 -24.13 0.000 -2.079881 -1.767391 x2 | -2.191231 .0876868 -24.99 0.000 -2.363094 -2.019368 x3 | .8047872 .106748 7.54 0.000 .5955649 1.014009 x12 | -1.63741 .4604744 -3.56 0.000 -2.539923 -.7348962 _cons | 6.187309 .1109696 55.76 0.000 5.969813 6.404806 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -99.351245 Iteration 1: log likelihood = -36.999429 Iteration 2: log likelihood = -36.606784 Iteration 3: log likelihood = -36.606592 Iteration 4: log likelihood = -36.606592 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 35.96734815 (1/df) Deviance = 17.98367 Pearson = 22.803379 (1/df) Pearson = 11.40169 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -36.60659163 AIC = 11.8876 BIC = 32.07552786 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.6043546 .2502527 -2.41 0.016 -1.094841 -.1138683 x2 | -2.108697 .0837181 -25.19 0.000 -2.272781 -1.944612 x3 | 1.857489 .2323725 7.99 0.000 1.402048 2.312931 x13 | -1.661116 .2668012 -6.23 0.000 -2.184037 -1.138195 _cons | 5.153219 .2337259 22.05 0.000 4.695125 5.611314 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -131.36451 Iteration 1: log likelihood = -30.2319 Iteration 2: log likelihood = -26.071306 Iteration 3: log likelihood = -26.032523 Iteration 4: log likelihood = -26.032519 Iteration 5: log likelihood = -26.032519 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 14.81920268 (1/df) Deviance = 7.409601 Pearson = 10.66950963 (1/df) Pearson = 5.334755 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -26.03251889 AIC = 8.866434 BIC = 10.92738238 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.282224 .0924288 -24.69 0.000 -2.463381 -2.101067 x2 | -3.998319 .2531542 -15.79 0.000 -4.494492 -3.502146 x3 | .0571855 .1315033 0.43 0.664 -.2005562 .3149272 x23 | 1.973466 .2667624 7.40 0.000 1.450621 2.49631 _cons | 6.945663 .1340785 51.80 0.000 6.682874 7.208452 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -126.14707 Iteration 1: log likelihood = -32.695971 Iteration 2: log likelihood = -20.045035 Iteration 3: log likelihood = -19.255375 Iteration 4: log likelihood = -19.248781 Iteration 5: log likelihood = -19.24878 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.251724435 (1/df) Deviance = 1.251724 Pearson = .6748933488 (1/df) Pearson = .6748933 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.24877977 AIC = 7.213937 BIC = -.6941857141 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.171815 .094782 -22.91 0.000 -2.357584 -1.986045 x2 | -3.818745 .2572812 -14.84 0.000 -4.323006 -3.314483 x3 | .1568425 .1322818 1.19 0.236 -.1024251 .4161101 x12 | -1.389231 .46332 -3.00 0.003 -2.297322 -.4811405 x23 | 1.873809 .267147 7.01 0.000 1.35021 2.397407 _cons | 6.835254 .1357114 50.37 0.000 6.569264 7.101243 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -111.02418 Iteration 1: log likelihood = -32.730854 Iteration 2: log likelihood = -22.391786 Iteration 3: log likelihood = -21.672435 Iteration 4: log likelihood = -21.666388 Iteration 5: log likelihood = -21.666386 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 6.086937488 (1/df) Deviance = 6.086937 Pearson = 4.19784191 (1/df) Pearson = 4.197842 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.6663863 AIC = 7.904682 BIC = 4.141027339 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.3512609 .2540005 -1.38 0.167 -.8490927 .1465709 x2 | -1.948671 .085853 -22.70 0.000 -2.11694 -1.780403 x3 | 1.998903 .232531 8.60 0.000 1.54315 2.454655 x12 | -1.87997 .4601288 -4.09 0.000 -2.781806 -.978134 x13 | -1.802529 .2669392 -6.75 0.000 -2.325721 -1.279338 _cons | 4.993194 .234499 21.29 0.000 4.533584 5.452803 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -100.98312 Iteration 1: log likelihood = -34.762067 Iteration 2: log likelihood = -26.18509 Iteration 3: log likelihood = -24.447896 Iteration 4: log likelihood = -24.085614 Iteration 5: log likelihood = -23.999929 Iteration 6: log likelihood = -23.980459 Iteration 7: log likelihood = -23.976255 Iteration 8: log likelihood = -23.975543 Iteration 9: log likelihood = -23.975465 Iteration 10: log likelihood = -23.975449 Iteration 11: log likelihood = -23.975446 Iteration 12: log likelihood = -23.975445 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 10.70505549 (1/df) Deviance = 10.70506 Pearson = 8.378373364 (1/df) Pearson = 8.378373 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.9754453 AIC = 8.564413 BIC = 8.759145342 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -18.871 2733.261 -0.01 0.994 -5375.964 5338.222 x2 | -20.48995 2733.261 -0.01 0.994 -5377.583 5336.603 x3 | -16.53315 2733.261 -0.01 0.995 -5373.626 5340.56 x13 | 16.60553 2733.261 0.01 0.995 -5340.487 5373.698 x23 | 18.4651 2733.261 0.01 0.995 -5338.628 5375.558 _cons | 23.53445 2733.261 0.01 0.993 -5333.558 5380.627 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 523.2455 .0121565 77.25005 74.12611 83.08778 3 5.58e-16 | 2. | 2 486.5352 .0123143 58.23471 56.73731 62.12653 2 4.78e-13 | 3. | 3 172.9875 .0546278 32.07553 22.80338 35.96735 2 .0000112 | 4. | 4 1038.636 .017977 10.92738 10.66951 14.8192 2 .0048211 | 5. | 5 930.0645 .0184176 -.6941857 .6748933 1.251724 1 .4113508 | |-------------------------------------------------------------------------------| 6. | 6 147.4064 .0549898 4.141027 4.197842 6.086937 1 .0404755 | 7. | 7 1.66e+10 7470715 8.759146 8.378373 10.70506 1 .0037971 | +-------------------------------------------------------------------------------+ nk is 1499 Buen modelo: Sistema 4to solo < estimacion (45 < 930) BICs = -0.69 4.14 . Estimacion subregistro 930 IC ( 675 ; 1185) Estimacion del total 2429 IC ( 2174 ; 2684) Estimacion BMA: 800 IC ( -11 ; 1611) Total registros = 1499 (note: file /tmp/model_info_gov_for_jud_todo.dta not found) (note: file /tmp/bma_info_gov_for_jud_todo.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_todo.dta not found) . . estimacion ong gov for _anod ong gov for cell values: 1 28 33 46 4 202 113 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -69.072555 Iteration 1: log likelihood = -57.23922 Iteration 2: log likelihood = -57.193892 Iteration 3: log likelihood = -57.193888 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 79.22270255 (1/df) Deviance = 26.40757 Pearson = 77.96242281 (1/df) Pearson = 25.98747 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -57.19388763 AIC = 17.48397 BIC = 73.3849721 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.217418 .1484999 -14.93 0.000 -2.508473 -1.926364 x2 | -1.302945 .1446086 -9.01 0.000 -1.586373 -1.019517 x3 | -1.837948 .143365 -12.82 0.000 -2.118938 -1.556957 _cons | 6.51149 .1553133 41.92 0.000 6.207081 6.815898 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -67.677809 Iteration 1: log likelihood = -55.738127 Iteration 2: log likelihood = -55.671811 Iteration 3: log likelihood = -55.671803 Iteration 4: log likelihood = -55.671803 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 76.17853351 (1/df) Deviance = 38.08927 Pearson = 87.41122712 (1/df) Pearson = 43.70561 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -55.6718031 AIC = 17.3348 BIC = 72.28671321 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.469747 .2112791 -11.69 0.000 -2.883846 -2.055647 x2 | -1.511318 .1919245 -7.87 0.000 -1.887484 -1.135153 x3 | -1.982815 .1730288 -11.46 0.000 -2.321945 -1.643684 x12 | .5091665 .2897861 1.76 0.079 -.0588039 1.077137 _cons | 6.710203 .1969481 34.07 0.000 6.324191 7.096214 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -49.355947 Iteration 1: log likelihood = -40.482039 Iteration 2: log likelihood = -40.354006 Iteration 3: log likelihood = -40.353822 Iteration 4: log likelihood = -40.353822 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 45.54257089 (1/df) Deviance = 22.77129 Pearson = 47.30719679 (1/df) Pearson = 23.6536 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -40.35382179 AIC = 12.95823 BIC = 41.65075059 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.923795 .2105293 -13.89 0.000 -3.336425 -2.511166 x2 | -1.760988 .1884446 -9.34 0.000 -2.130332 -1.391643 x3 | -2.465687 .1983837 -12.43 0.000 -2.854511 -2.076862 x13 | 1.687982 .2868473 5.88 0.000 1.125772 2.250192 _cons | 7.069256 .2011513 35.14 0.000 6.675006 7.463505 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -40.810816 Iteration 1: log likelihood = -22.07326 Iteration 2: log likelihood = -21.103804 Iteration 3: log likelihood = -21.09536 Iteration 4: log likelihood = -21.095359 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 7.025645051 (1/df) Deviance = 3.512823 Pearson = 7.180698162 (1/df) Pearson = 3.590349 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.09535888 AIC = 7.455817 BIC = 3.133824753 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.638057 .1387942 -11.80 0.000 -1.910088 -1.366025 x2 | -.2062271 .1989757 -1.04 0.300 -.5962122 .1837581 x3 | -.6606998 .2051653 -3.22 0.001 -1.062816 -.2585831 x23 | -3.167941 .4964278 -6.38 0.000 -4.140922 -2.194961 _cons | 5.466698 .2024919 27.00 0.000 5.069821 5.863575 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -39.828492 Iteration 1: log likelihood = -18.514802 Iteration 2: log likelihood = -17.710406 Iteration 3: log likelihood = -17.702612 Iteration 4: log likelihood = -17.70261 Iteration 5: log likelihood = -17.70261 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .2401479471 (1/df) Deviance = .2401479 Pearson = .2770702028 (1/df) Pearson = .2770702 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.70261032 AIC = 6.772174 BIC = -1.705762202 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.23088 .1978701 -6.22 0.000 -1.618699 -.8430619 x2 | .2468483 .2565903 0.96 0.336 -.2560595 .7497561 x3 | -.3321338 .2281275 -1.46 0.145 -.7792555 .1149879 x12 | -.7297001 .2801602 -2.60 0.009 -1.278804 -.1805963 x23 | -3.496508 .5063497 -6.91 0.000 -4.488935 -2.50408 _cons | 5.059522 .2467625 20.50 0.000 4.575876 5.543167 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -35.318484 Iteration 1: log likelihood = -27.124944 Iteration 2: log likelihood = -26.823392 Iteration 3: log likelihood = -26.821655 Iteration 4: log likelihood = -26.821655 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 18.47823687 (1/df) Deviance = 18.47824 Pearson = 14.4436535 (1/df) Pearson = 14.44365 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -26.82165478 AIC = 9.377616 BIC = 16.53232672 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.657979 .5298231 -8.79 0.000 -5.696414 -3.619545 x2 | -3.341093 .5087726 -6.57 0.000 -4.338269 -2.343917 x3 | -3.921973 .5049262 -7.77 0.000 -4.91161 -2.932336 x12 | 2.338941 .5531641 4.23 0.000 1.25476 3.423123 x13 | 3.144269 .5457799 5.76 0.000 2.07456 4.213978 _cons | 8.649361 .5136147 16.84 0.000 7.642695 9.656027 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -34.885361 Iteration 1: log likelihood = -18.819422 Iteration 2: log likelihood = -17.615589 Iteration 3: log likelihood = -17.592216 Iteration 4: log likelihood = -17.592178 Iteration 5: log likelihood = -17.592178 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .0192826131 (1/df) Deviance = .0192826 Pearson = .0187713933 (1/df) Pearson = .0187714 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.59217766 AIC = 6.740622 BIC = -1.926627536 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.976063 .2016551 -9.80 0.000 -2.3713 -1.580826 x2 | -.4964369 .2396944 -2.07 0.038 -.9662293 -.0266445 x3 | -1.076204 .2667915 -4.03 0.000 -1.599106 -.5533021 x13 | .7402498 .2803989 2.64 0.008 .190678 1.289822 x23 | -2.877732 .5141038 -5.60 0.000 -3.885357 -1.870107 _cons | 5.804705 .2498077 23.24 0.000 5.31509 6.294319 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 672.8279 .0241222 73.38497 77.96243 79.2227 3 8.40e-17 | 2. | 2 820.7368 .0387885 72.28671 87.41122 76.17854 2 1.04e-19 | 3. | 3 1175.273 .0404619 41.65075 47.3072 45.54257 2 5.34e-11 | 4. | 4 236.6774 .041003 3.133825 7.180698 7.025645 2 .0275887 | 5. | 5 157.5152 .0608917 -1.705762 .2770702 .2401479 1 .598628 | |-------------------------------------------------------------------------------| 6. | 6 5706.5 .2638 16.53233 14.44365 18.47824 1 .0001444 | 7. | 7 331.8571 .0624039 -1.926628 .0187714 .0192826 1 .8910239 | +-------------------------------------------------------------------------------+ nk is 427 Modelo aceptable: Sistema 4to solo < estimacion (408 < 499) BICs = -1.93 -1.71 3.13 Estimacion subregistro 332 IC ( 165 ; 499) Estimacion del total 759 IC ( 592 ; 926) Estimacion BMA: 234 IC ( -38 ; 506) Total registros = 427 (note: file /tmp/model_info_ong_gov_for_anod.dta not found) (note: file /tmp/bma_info_ong_gov_for_anod.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_anod.dta not found) . estimacion ong gov jud _anod ong gov jud cell values: 19 10 43 36 110 96 509 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -44.230768 Iteration 1: log likelihood = -23.610583 Iteration 2: log likelihood = -23.39009 Iteration 3: log likelihood = -23.389904 Iteration 4: log likelihood = -23.389904 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 5.788131631 (1/df) Deviance = 1.929377 Pearson = 6.485023823 (1/df) Pearson = 2.161675 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.38990357 AIC = 7.825687 BIC = -.0495988163 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.329769 .1105525 -21.07 0.000 -2.546447 -2.11309 x2 | -1.43078 .0888909 -16.10 0.000 -1.605003 -1.256557 x3 | .2380098 .1101979 2.16 0.031 .0220258 .4539938 _cons | 5.97828 .117298 50.97 0.000 5.74838 6.20818 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -38.300538 Iteration 1: log likelihood = -21.625022 Iteration 2: log likelihood = -21.267905 Iteration 3: log likelihood = -21.267038 Iteration 4: log likelihood = -21.267038 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.54240022 (1/df) Deviance = .7712001 Pearson = 1.512376095 (1/df) Pearson = .756188 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.26703787 AIC = 7.504868 BIC = -2.349420078 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.464899 .1313473 -18.77 0.000 -2.722335 -2.207463 x2 | -1.50647 .0972021 -15.50 0.000 -1.696983 -1.315958 x3 | .1916674 .1133852 1.69 0.091 -.0305635 .4138984 x12 | .5043183 .2378849 2.12 0.034 .0380725 .9705642 _cons | 6.040781 .1217409 49.62 0.000 5.802173 6.279388 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -45.757705 Iteration 1: log likelihood = -23.491228 Iteration 2: log likelihood = -23.318496 Iteration 3: log likelihood = -23.318424 Iteration 4: log likelihood = -23.318424 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 5.645172298 (1/df) Deviance = 2.822586 Pearson = 6.258450668 (1/df) Pearson = 3.129225 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.31842391 AIC = 8.090978 BIC = 1.753352 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.390159 .1948705 -12.27 0.000 -2.772099 -2.00822 x2 | -1.442253 .0943129 -15.29 0.000 -1.627103 -1.257403 x3 | .2093045 .1336073 1.57 0.117 -.0525609 .47117 x13 | .0891885 .2360489 0.38 0.706 -.3734588 .5518357 _cons | 6.006601 .1389662 43.22 0.000 5.734233 6.27897 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -43.457244 Iteration 1: log likelihood = -23.443455 Iteration 2: log likelihood = -23.218818 Iteration 3: log likelihood = -23.218729 Iteration 4: log likelihood = -23.218729 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 5.445781554 (1/df) Deviance = 2.722891 Pearson = 6.069095887 (1/df) Pearson = 3.034548 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.21872853 AIC = 8.062494 BIC = 1.553961256 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.295616 .1236426 -18.57 0.000 -2.537951 -2.053281 x2 | -1.311642 .2232263 -5.88 0.000 -1.749157 -.8741265 x3 | .338467 .2054449 1.65 0.099 -.0641977 .7411316 x23 | -.1420937 .2437078 -0.58 0.560 -.6197523 .3355649 _cons | 5.879135 .2075217 28.33 0.000 5.4724 6.28587 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -38.291045 Iteration 1: log likelihood = -21.62273 Iteration 2: log likelihood = -21.265347 Iteration 3: log likelihood = -21.264478 Iteration 4: log likelihood = -21.264478 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.537279995 (1/df) Deviance = 1.53728 Pearson = 1.507940483 (1/df) Pearson = 1.50794 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.26447775 AIC = 7.789851 BIC = -.4086301545 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.471248 .1588095 -15.56 0.000 -2.782509 -2.159987 x2 | -1.523037 .2510602 -6.07 0.000 -2.015106 -1.030968 x3 | .1776812 .2259062 0.79 0.432 -.2650868 .6204491 x12 | .5106676 .2540818 2.01 0.044 .0126763 1.008659 x23 | .0186921 .2611886 0.07 0.943 -.4932281 .5306124 _cons | 6.054767 .2302134 26.30 0.000 5.603557 6.505977 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -38.279205 Iteration 1: log likelihood = -21.416817 Iteration 2: log likelihood = -21.037612 Iteration 3: log likelihood = -21.036693 Iteration 4: log likelihood = -21.036693 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.081710594 (1/df) Deviance = 1.081711 Pearson = 1.066391263 (1/df) Pearson = 1.066391 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.03669305 AIC = 7.724769 BIC = -.8641995551 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.580358 .2158942 -11.95 0.000 -3.003503 -2.157213 x2 | -1.531968 .1051454 -14.57 0.000 -1.738049 -1.325887 x3 | .1361322 .1396695 0.97 0.330 -.1376151 .4098794 x12 | .5298156 .2412396 2.20 0.028 .0569948 1.002637 x13 | .1623608 .2395323 0.68 0.498 -.3071139 .6318356 _cons | 6.096316 .146534 41.60 0.000 5.809114 6.383517 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -42.255345 Iteration 1: log likelihood = -23.638507 Iteration 2: log likelihood = -23.213325 Iteration 3: log likelihood = -23.212777 Iteration 4: log likelihood = -23.212777 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 5.433878712 (1/df) Deviance = 5.433879 Pearson = 6.083670628 (1/df) Pearson = 6.083671 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.21277711 AIC = 8.346508 BIC = 3.487968563 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.261763 .33229 -6.81 0.000 -2.91304 -1.610487 x2 | -1.280934 .3574602 -3.58 0.000 -1.981543 -.5803248 x3 | .372809 .3743703 1.00 0.319 -.3609433 1.106561 x13 | -.0392078 .3579961 -0.11 0.913 -.7408673 .6624517 x23 | -.1728018 .3705959 -0.47 0.641 -.8991564 .5535528 _cons | 5.845282 .3717451 15.72 0.000 5.116675 6.573889 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 394.7608 .0137588 -.0495988 6.485024 5.788132 3 .090255 | 2. | 2 420.2209 .0148208 -2.34942 1.512376 1.5424 2 .4694526 | 3. | 3 406.1007 .0193116 1.753352 6.258451 5.645172 2 .0437517 | 4. | 4 357.5 .0430653 1.553961 6.069096 5.445782 2 .0480964 | 5. | 5 426.1395 .0529982 -.4086302 1.507941 1.53728 1 .2194536 | |-------------------------------------------------------------------------------| 6. | 6 444.2182 .0214722 -.8641996 1.066391 1.081711 1 .301762 | 7. | 7 345.6 .1381944 3.487968 6.083671 5.433879 1 .0136437 | +-------------------------------------------------------------------------------+ nk is 823 Buen modelo: Sistema 4to solo < estimacion (12 < 420) BICs = -2.35 -0.86 -0.41 Estimacion subregistro 420 IC ( 312 ; 528) Estimacion del total 1243 IC ( 1135 ; 1351) Estimacion BMA: 416 IC ( 265 ; 567) Total registros = 823 (note: file /tmp/model_info_ong_gov_jud_anod.dta not found) (note: file /tmp/bma_info_ong_gov_jud_anod.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_anod.dta not found) . estimacion ong for jud _anod ong for jud cell values: 33 1 29 45 105 12 514 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -91.620165 Iteration 1: log likelihood = -62.693432 Iteration 2: log likelihood = -62.569036 Iteration 3: log likelihood = -62.568985 Iteration 4: log likelihood = -62.568985 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 88.02885306 (1/df) Deviance = 29.34295 Pearson = 90.2419974 (1/df) Pearson = 30.08067 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -62.56898487 AIC = 19.01971 BIC = 82.19112262 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.988696 .1083654 -18.35 0.000 -2.201088 -1.776304 x2 | -1.59751 .096559 -16.54 0.000 -1.786762 -1.408258 x3 | 1.148061 .1494076 7.68 0.000 .8552275 1.440895 _cons | 5.0629 .1548948 32.69 0.000 4.759311 5.366488 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -74.364707 Iteration 1: log likelihood = -54.51603 Iteration 2: log likelihood = -54.21656 Iteration 3: log likelihood = -54.21592 Iteration 4: log likelihood = -54.21592 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 71.32272261 (1/df) Deviance = 35.66136 Pearson = 71.4684025 (1/df) Pearson = 35.7342 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -54.21591964 AIC = 16.91883 BIC = 67.43090232 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.236265 .1303941 -17.15 0.000 -2.491832 -1.980697 x2 | -1.778156 .1097092 -16.21 0.000 -1.993182 -1.56313 x3 | 1.057551 .152412 6.94 0.000 .7588289 1.356273 x12 | 1.000451 .2344385 4.27 0.000 .5409604 1.459942 _cons | 5.184672 .1586661 32.68 0.000 4.873693 5.495652 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -68.651839 Iteration 1: log likelihood = -44.057177 Iteration 2: log likelihood = -43.804631 Iteration 3: log likelihood = -43.804521 Iteration 4: log likelihood = -43.804521 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 50.49992608 (1/df) Deviance = 25.24996 Pearson = 57.04132091 (1/df) Pearson = 28.52066 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -43.80452138 AIC = 13.94415 BIC = 46.60810579 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.3107178 .3330033 -0.93 0.351 -.9633923 .3419567 x2 | -1.442253 .0943129 -15.29 0.000 -1.627103 -1.257403 x3 | 2.288746 .3012766 7.60 0.000 1.698255 2.879237 x13 | -1.990253 .3586582 -5.55 0.000 -2.69321 -1.287296 _cons | 3.92716 .3036911 12.93 0.000 3.331936 4.522383 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -85.40327 Iteration 1: log likelihood = -38.788567 Iteration 2: log likelihood = -37.165252 Iteration 3: log likelihood = -37.150706 Iteration 4: log likelihood = -37.150705 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 37.1922925 (1/df) Deviance = 18.59615 Pearson = 46.01560643 (1/df) Pearson = 23.0078 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.15070458 AIC = 12.04306 BIC = 33.3004722 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.304171 .132128 -17.44 0.000 -2.563137 -2.045205 x2 | -3.64105 .3370125 -10.80 0.000 -4.301583 -2.980518 x3 | .0911096 .1962038 0.46 0.642 -.2934428 .475662 x23 | 2.271195 .3502362 6.48 0.000 1.584744 2.957645 _cons | 6.110834 .1991985 30.68 0.000 5.720412 6.501255 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -50.249419 Iteration 1: log likelihood = -21.034341 Iteration 2: log likelihood = -19.670924 Iteration 3: log likelihood = -19.657322 Iteration 4: log likelihood = -19.657321 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 2.205526089 (1/df) Deviance = 2.205526 Pearson = 1.791742953 (1/df) Pearson = 1.791743 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.65732138 AIC = 7.330663 BIC = .2596159399 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.874927 .1908619 -15.06 0.000 -3.24901 -2.500845 x2 | -4.371746 .3708073 -11.79 0.000 -5.098515 -3.644977 x3 | -.4393667 .2381281 -1.85 0.065 -.9060891 .0273558 x12 | 1.639114 .2727399 6.01 0.000 1.104554 2.173674 x23 | 2.801671 .3753324 7.46 0.000 2.066033 3.537309 _cons | 6.68159 .2421787 27.59 0.000 6.206928 7.156251 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -57.534657 Iteration 1: log likelihood = -38.491015 Iteration 2: log likelihood = -38.165093 Iteration 3: log likelihood = -38.163961 Iteration 4: log likelihood = -38.163961 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 39.21880464 (1/df) Deviance = 39.2188 Pearson = 31.90702756 (1/df) Pearson = 31.90703 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -38.16396066 AIC = 12.61827 BIC = 37.27289449 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.6225943 .3475573 -1.79 0.073 -1.303794 .0586054 x2 | -1.588263 .107095 -14.83 0.000 -1.798165 -1.378361 x3 | 2.169054 .3047247 7.12 0.000 1.571804 2.766303 x12 | .8105583 .2332265 3.48 0.001 .3534428 1.267674 x13 | -1.870561 .3615595 -5.17 0.000 -2.579204 -1.161917 _cons | 4.07317 .3079004 13.23 0.000 3.469696 4.676643 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -69.315167 Iteration 1: log likelihood = -40.20038 Iteration 2: log likelihood = -37.387241 Iteration 3: log likelihood = -37.138616 Iteration 4: log likelihood = -37.134553 Iteration 5: log likelihood = -37.134548 Iteration 6: log likelihood = -37.134548 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 37.15997871 (1/df) Deviance = 37.15998 Pearson = 45.86454737 (1/df) Pearson = 45.86455 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.13454769 AIC = 12.32416 BIC = 35.21406856 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.484907 1.040833 -2.39 0.017 -4.524902 -.4449115 x2 | -3.806662 1.01105 -3.77 0.000 -5.788284 -1.825041 x3 | -.0899169 1.052399 -0.09 0.932 -2.152582 1.972748 x13 | .1839358 1.049323 0.18 0.861 -1.872699 2.24057 x23 | 2.436807 1.015534 2.40 0.016 .4463959 4.427218 _cons | 6.291569 1.051454 5.98 0.000 4.230757 8.352381 ------------------------------------------------------------------------------ +------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |------------------------------------------------------------------------------| 1. | 1 158.0481 .0239924 82.19112 90.242 88.02885 3 1.94e-19 | 2. | 2 178.515 .0251749 67.4309 71.4684 71.32272 2 3.03e-16 | 3. | 3 50.76259 .0922283 46.6081 57.04132 50.49993 2 4.11e-13 | 4. | 4 450.7143 .03968 33.30047 46.01561 37.19229 2 1.02e-10 | 5. | 5 797.5862 .0586505 .2596159 1.791743 2.205526 1 .180714 | |------------------------------------------------------------------------------| 6. | 6 58.74286 .0948027 37.2729 31.90703 39.2188 1 1.62e-08 | 7. | 7 540 1.105556 35.21407 45.86455 37.15998 1 1.27e-11 | +------------------------------------------------------------------------------+ nk is 739 Buen modelo: Sistema 4to solo < estimacion (96 < 798) BICs = 0.26 33.30 35.21 Estimacion subregistro 798 IC ( 416 ; 1180) Estimacion del total 1537 IC ( 1155 ; 1919) Estimacion BMA: 798 IC ( 416 ; 1180) Total registros = 739 (note: file /tmp/model_info_ong_for_jud_anod.dta not found) (note: file /tmp/bma_info_ong_for_jud_anod.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_anod.dta not found) . estimacion gov for jud _anod gov for jud cell values: 5 0 124 106 133 13 419 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -88.218474 Iteration 1: log likelihood = -78.77359 Iteration 2: log likelihood = -78.760806 Iteration 3: log likelihood = -78.760805 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 121.8573459 (1/df) Deviance = 40.61912 Pearson = 107.7601939 (1/df) Pearson = 35.92006 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -78.76080549 AIC = 23.64594 BIC = 116.0196155 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.218136 .083945 -14.51 0.000 -1.382665 -1.053607 x2 | -1.760944 .0949447 -18.55 0.000 -1.947032 -1.574856 x3 | .6699045 .1109239 6.04 0.000 .4524977 .8873114 _cons | 5.435933 .118372 45.92 0.000 5.203928 5.667938 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -87.069621 Iteration 1: log likelihood = -55.102615 Iteration 2: log likelihood = -51.905496 Iteration 3: log likelihood = -51.852361 Iteration 4: log likelihood = -51.852165 Iteration 5: log likelihood = -51.852165 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 68.04006449 (1/df) Deviance = 34.02003 Pearson = 59.78953897 (1/df) Pearson = 29.89477 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -51.85216477 AIC = 16.24348 BIC = 64.1482442 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.9742465 .0890314 -10.94 0.000 -1.148745 -.7997482 x2 | -1.428719 .1021179 -13.99 0.000 -1.628867 -1.228572 x3 | .789221 .1105448 7.14 0.000 .5725572 1.005885 x12 | -2.399922 .4634392 -5.18 0.000 -3.308246 -1.491598 _cons | 5.24865 .1208586 43.43 0.000 5.011771 5.485528 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -68.980019 Iteration 1: log likelihood = -57.495117 Iteration 2: log likelihood = -57.263521 Iteration 3: log likelihood = -57.263054 Iteration 4: log likelihood = -57.263054 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 78.8618426 (1/df) Deviance = 39.43092 Pearson = 55.60688556 (1/df) Pearson = 27.80344 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -57.26305383 AIC = 17.78944 BIC = 74.97002231 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .3575152 .3038631 1.18 0.239 -.2380455 .9530759 x2 | -1.548179 .0937401 -16.52 0.000 -1.731906 -1.364452 x3 | 2.007624 .2910505 6.90 0.000 1.437176 2.578073 x13 | -1.811251 .319212 -5.67 0.000 -2.436895 -1.185607 _cons | 4.113128 .2927632 14.05 0.000 3.539323 4.686934 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -68.39676 Iteration 1: log likelihood = -39.391891 Iteration 2: log likelihood = -37.852601 Iteration 3: log likelihood = -37.840422 Iteration 4: log likelihood = -37.840419 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 40.01657251 (1/df) Deviance = 20.00829 Pearson = 29.86580598 (1/df) Pearson = 14.9329 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.84041878 AIC = 12.24012 BIC = 36.12475221 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.477013 .09758 -15.14 0.000 -1.668267 -1.28576 x2 | -3.781149 .3044143 -12.42 0.000 -4.37779 -3.184508 x3 | -.0489893 .1326145 -0.37 0.712 -.3089089 .2109303 x23 | 2.411293 .3189922 7.56 0.000 1.78608 3.036507 _cons | 6.140452 .1376801 44.60 0.000 5.870604 6.4103 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -71.805994 Iteration 1: log likelihood = -22.79806 Iteration 2: log likelihood = -18.4152 Iteration 3: log likelihood = -18.290596 Iteration 4: log likelihood = -18.290247 Iteration 5: log likelihood = -18.290247 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .9162281829 (1/df) Deviance = .9162282 Pearson = .487145125 (1/df) Pearson = .4871451 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.29024662 AIC = 6.94007 BIC = -1.029681966 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.217589 .1022309 -11.91 0.000 -1.417958 -1.017221 x2 | -3.349752 .3115044 -10.75 0.000 -3.96029 -2.739215 x3 | .1568425 .1322818 1.19 0.236 -.1024251 .4161101 x12 | -2.156579 .466155 -4.63 0.000 -3.070226 -1.242932 x23 | 2.205462 .3188541 6.92 0.000 1.580519 2.830404 _cons | 5.881028 .1410146 41.71 0.000 5.604645 6.157412 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -58.401293 Iteration 1: log likelihood = -23.780856 Iteration 2: log likelihood = -20.921847 Iteration 3: log likelihood = -20.875717 Iteration 4: log likelihood = -20.875601 Iteration 5: log likelihood = -20.875601 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 6.086937488 (1/df) Deviance = 6.086937 Pearson = 4.197842938 (1/df) Pearson = 4.197843 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.87560127 AIC = 7.678743 BIC = 4.141027339 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .9294617 .310411 2.99 0.003 .3210674 1.537856 x2 | -1.147522 .099526 -11.53 0.000 -1.342589 -.9524543 x3 | 2.3254 .2905888 8.00 0.000 1.755856 2.894943 x12 | -2.68112 .462875 -5.79 0.000 -3.588338 -1.773901 x13 | -2.129026 .3187911 -6.68 0.000 -2.753846 -1.504207 _cons | 3.712471 .2946668 12.60 0.000 3.134935 4.290007 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -65.412921 Iteration 1: log likelihood = -40.805962 Iteration 2: log likelihood = -36.246775 Iteration 3: log likelihood = -35.38166 Iteration 4: log likelihood = -35.196307 Iteration 5: log likelihood = -35.150889 Iteration 6: log likelihood = -35.141426 Iteration 7: log likelihood = -35.139417 Iteration 8: log likelihood = -35.138966 Iteration 9: log likelihood = -35.138852 Iteration 10: log likelihood = -35.13883 Iteration 11: log likelihood = -35.138826 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 34.61338759 (1/df) Deviance = 34.61339 Pearson = 26.45391845 (1/df) Pearson = 26.45392 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -35.13882632 AIC = 11.75395 BIC = 32.66747744 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -16.39442 1006.992 -0.02 0.987 -1990.062 1957.273 x2 | -18.49288 1006.992 -0.02 0.985 -1992.161 1955.175 x3 | -14.97078 1006.992 -0.01 0.988 -1988.639 1958.697 x13 | 14.94068 1006.992 0.01 0.988 -1958.727 1988.609 x23 | 17.12303 1006.992 0.02 0.986 -1956.545 1990.791 _cons | 21.05786 1006.992 0.02 0.983 -1952.61 1994.726 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 229.5068 .0140119 116.0196 107.7602 121.8573 3 3.33e-23 | 2. | 2 190.3092 .0146068 64.14825 59.78954 68.04006 2 1.04e-13 | 3. | 3 61.13768 .0857103 74.97002 55.60688 78.86184 2 8.42e-13 | 4. | 4 464.2636 .0189558 36.12475 29.86581 40.01657 2 3.27e-07 | 5. | 5 358.1774 .0198851 -1.029682 .4871451 .9162282 1 .4852036 | |-------------------------------------------------------------------------------| 6. | 6 40.95489 .0868285 4.141027 4.197843 6.086937 1 .0404754 | 7. | 7 1.40e+09 1014033 32.66748 26.45392 34.61339 1 2.70e-07 | +-------------------------------------------------------------------------------+ nk is 800 Buen modelo: Sistema 4to solo < estimacion (35 < 358) BICs = -1.03 4.14 . Estimacion subregistro 358 IC ( 252 ; 464) Estimacion del total 1158 IC ( 1052 ; 1264) Estimacion BMA: 308 IC ( -17 ; 633) Total registros = 800 (note: file /tmp/model_info_gov_for_jud_anod.dta not found) (note: file /tmp/bma_info_gov_for_jud_anod.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_anod.dta not found) . . estimacion ong gov for _01_04 ong gov for cell values: 0 27 31 38 3 121 85 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -55.343345 Iteration 1: log likelihood = -50.602813 Iteration 2: log likelihood = -50.576551 Iteration 3: log likelihood = -50.576544 Iteration 4: log likelihood = -50.576544 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 69.34673356 (1/df) Deviance = 23.11558 Pearson = 61.59832268 (1/df) Pearson = 20.53277 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -50.57654445 AIC = 15.5933 BIC = 63.50900311 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.801723 .1586687 -11.36 0.000 -2.112708 -1.490739 x2 | -1.24948 .1562891 -7.99 0.000 -1.555801 -.9431592 x3 | -1.546611 .155977 -9.92 0.000 -1.85232 -1.240902 _cons | 5.920956 .1705264 34.72 0.000 5.58673 6.255182 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -53.859833 Iteration 1: log likelihood = -49.044016 Iteration 2: log likelihood = -49.016215 Iteration 3: log likelihood = -49.016206 Iteration 4: log likelihood = -49.016206 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 66.22605678 (1/df) Deviance = 33.11303 Pearson = 66.93321768 (1/df) Pearson = 33.46661 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -49.01620606 AIC = 15.4332 BIC = 62.33423648 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.075812 .2261057 -9.18 0.000 -2.518971 -1.632653 x2 | -1.489637 .2114132 -7.05 0.000 -1.903999 -1.075275 x3 | -1.699386 .1865157 -9.11 0.000 -2.06495 -1.333822 x12 | .5513671 .3102021 1.78 0.075 -.0566178 1.159352 _cons | 6.142037 .215761 28.47 0.000 5.719154 6.564921 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -44.607126 Iteration 1: log likelihood = -40.542925 Iteration 2: log likelihood = -40.520991 Iteration 3: log likelihood = -40.520986 Iteration 4: log likelihood = -40.520986 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 49.23561616 (1/df) Deviance = 24.61781 Pearson = 47.09320587 (1/df) Pearson = 23.5466 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -40.52098575 AIC = 13.006 BIC = 45.34379586 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.435142 .227041 -10.73 0.000 -2.880134 -1.990149 x2 | -1.635755 .1995666 -8.20 0.000 -2.026899 -1.244612 x3 | -2.132192 .2180061 -9.78 0.000 -2.559476 -1.704908 x13 | 1.391792 .3084953 4.51 0.000 .7871524 1.996432 _cons | 6.431546 .2192973 29.33 0.000 6.001731 6.861361 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -30.41599 Iteration 1: log likelihood = -18.546051 Iteration 2: log likelihood = -18.002849 Iteration 3: log likelihood = -17.998351 Iteration 4: log likelihood = -17.998349 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 4.19034275 (1/df) Deviance = 2.095171 Pearson = 3.592722253 (1/df) Pearson = 1.796361 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.99834904 AIC = 6.570957 BIC = .2985224522 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.281891 .1484118 -8.64 0.000 -1.572773 -.9910094 x2 | -.1671795 .2157976 -0.77 0.439 -.5901351 .255776 x3 | -.4108016 .2200739 -1.87 0.062 -.8421386 .0205354 x23 | -3.487798 .6233157 -5.60 0.000 -4.709474 -2.266122 _cons | 4.919477 .2198678 22.37 0.000 4.488544 5.35041 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -30.096942 Iteration 1: log likelihood = -17.111871 Iteration 2: log likelihood = -16.50708 Iteration 3: log likelihood = -16.500762 Iteration 4: log likelihood = -16.500758 Iteration 5: log likelihood = -16.500758 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.195161448 (1/df) Deviance = 1.195161 Pearson = .6664668701 (1/df) Pearson = .6664669 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.50075839 AIC = 6.428788 BIC = -.7507487008 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.008664 .209816 -4.81 0.000 -1.419896 -.5974322 x2 | .1539638 .2802451 0.55 0.583 -.3953065 .7032341 x3 | -.203599 .2420204 -0.84 0.400 -.6779501 .2707522 x12 | -.5157806 .298537 -1.73 0.084 -1.100902 .0693411 x23 | -3.695001 .6313984 -5.85 0.000 -4.932519 -2.457483 _cons | 4.64625 .2652142 17.52 0.000 4.12644 5.16606 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -33.498463 Iteration 1: log likelihood = -29.00164 Iteration 2: log likelihood = -28.821252 Iteration 3: log likelihood = -28.820357 Iteration 4: log likelihood = -28.820357 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 25.83435818 (1/df) Deviance = 25.83436 Pearson = 17.91571906 (1/df) Pearson = 17.91572 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -28.82035676 AIC = 9.948673 BIC = 23.88844803 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.295684 .6105925 -7.04 0.000 -5.492423 -3.098945 x2 | -3.344039 .5874504 -5.69 0.000 -4.495421 -2.192657 x3 | -3.697178 .5844637 -6.33 0.000 -4.842706 -2.55165 x12 | 2.405769 .6297839 3.82 0.000 1.171415 3.640123 x13 | 2.956778 .6238914 4.74 0.000 1.733974 4.179583 _cons | 8.139829 .594443 13.69 0.000 6.974743 9.304916 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -28.525441 Iteration 1: log likelihood = -17.493463 Iteration 2: log likelihood = -16.82509 Iteration 3: log likelihood = -16.822129 Iteration 4: log likelihood = -16.822128 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.837901279 (1/df) Deviance = 1.837901 Pearson = 1.084149426 (1/df) Pearson = 1.084149 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.82212831 AIC = 6.520608 BIC = -.10800887 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.499954 .2128415 -7.05 0.000 -1.917115 -1.082792 x2 | -.3417493 .2516999 -1.36 0.175 -.835072 .1515734 x3 | -.6857363 .2884411 -2.38 0.017 -1.25107 -.1204022 x13 | .4566041 .2981999 1.53 0.126 -.1278571 1.041065 x23 | -3.313228 .6366371 -5.20 0.000 -4.561014 -2.065443 _cons | 5.13754 .2676141 19.20 0.000 4.613026 5.662054 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 372.768 .0290793 63.509 61.59832 69.34673 3 2.68e-13 | 2. | 2 465 .0465528 62.33424 66.93322 66.22606 2 2.92e-15 | 3. | 3 621.1334 .0480913 45.3438 47.0932 49.23561 2 5.94e-11 | 4. | 4 136.931 .0483419 .2985224 3.592722 4.190343 2 .1659015 | 5. | 5 104.1936 .0703386 -.7507487 .6664669 1.195161 1 .4142861 | |-------------------------------------------------------------------------------| 6. | 6 3428.333 .3533625 23.88845 17.91572 25.83436 1 .0000231 | 7. | 7 170.2963 .0716173 -.1080089 1.084149 1.837901 1 .2977712 | +-------------------------------------------------------------------------------+ nk is 305 Mal modelo: Sistema 4to solo > estimacion (268 > 161) (note: file /tmp/model_info_ong_gov_for_01_04.dta not found) (note: file /tmp/bma_info_ong_gov_for_01_04.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_01_04.dta not found) . estimacion ong gov jud _01_04 ong gov jud cell values: 18 9 39 30 69 55 344 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -34.12264 Iteration 1: log likelihood = -23.111007 Iteration 2: log likelihood = -23.067113 Iteration 3: log likelihood = -23.067104 Iteration 4: log likelihood = -23.067104 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 6.991103562 (1/df) Deviance = 2.330368 Pearson = 7.761140427 (1/df) Pearson = 2.587047 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.06710397 AIC = 7.733458 BIC = 1.153373115 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.989127 .1204262 -16.52 0.000 -2.225158 -1.753096 x2 | -1.454569 .1063078 -13.68 0.000 -1.662929 -1.24621 x3 | .3606997 .131832 2.74 0.006 .1023136 .6190857 _cons | 5.453946 .1403939 38.85 0.000 5.178779 5.729113 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -28.888583 Iteration 1: log likelihood = -20.223966 Iteration 2: log likelihood = -20.145816 Iteration 3: log likelihood = -20.145737 Iteration 4: log likelihood = -20.145737 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.148369092 (1/df) Deviance = .5741845 Pearson = 1.123788777 (1/df) Pearson = .5618944 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.14573673 AIC = 7.184496 BIC = -2.743451206 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.163881 .1441901 -15.01 0.000 -2.446488 -1.881273 x2 | -1.577706 .1198438 -13.16 0.000 -1.812595 -1.342816 x3 | .2929871 .1362894 2.15 0.032 .0258648 .5601095 x12 | .6394361 .256695 2.49 0.013 .1363231 1.142549 _cons | 5.547655 .1465666 37.85 0.000 5.260389 5.83492 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -34.977017 Iteration 1: log likelihood = -23.13795 Iteration 2: log likelihood = -23.060275 Iteration 3: log likelihood = -23.060245 Iteration 4: log likelihood = -23.060245 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 6.977386465 (1/df) Deviance = 3.488693 Pearson = 7.729617507 (1/df) Pearson = 3.864809 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.06024542 AIC = 8.017213 BIC = 3.085566167 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.011871 .228637 -8.80 0.000 -2.459992 -1.563751 x2 | -1.459099 .1133048 -12.88 0.000 -1.681173 -1.237026 x3 | .3480146 .1704557 2.04 0.041 .0139275 .6821017 x13 | .031475 .2687751 0.12 0.907 -.4953144 .5582645 _cons | 5.466433 .1761244 31.04 0.000 5.121235 5.81163 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -33.834334 Iteration 1: log likelihood = -23.0041 Iteration 2: log likelihood = -22.920695 Iteration 3: log likelihood = -22.920651 Iteration 4: log likelihood = -22.920651 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 6.698198412 (1/df) Deviance = 3.349099 Pearson = 7.420065377 (1/df) Pearson = 3.710033 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.92065139 AIC = 7.977329 BIC = 2.806378114 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.958814 .1314849 -14.90 0.000 -2.216519 -1.701108 x2 | -1.333055 .2494738 -5.34 0.000 -1.822015 -.8440957 x3 | .4560965 .2218629 2.06 0.040 .0212532 .8909398 x23 | -.1490715 .2763013 -0.54 0.590 -.6906121 .3924691 _cons | 5.360011 .2249924 23.82 0.000 4.919034 5.800988 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -28.876519 Iteration 1: log likelihood = -20.212703 Iteration 2: log likelihood = -20.134241 Iteration 3: log likelihood = -20.134161 Iteration 4: log likelihood = -20.134161 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.125218054 (1/df) Deviance = 1.125218 Pearson = 1.102922518 (1/df) Pearson = 1.102923 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.13416121 AIC = 7.466903 BIC = -.8206920949 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.17708 .1689615 -12.89 0.000 -2.508239 -1.845921 x2 | -1.616393 .2809775 -5.75 0.000 -2.167098 -1.065687 x3 | .2623643 .2428464 1.08 0.280 -.2136059 .7383344 x12 | .6526353 .2713845 2.40 0.016 .1207314 1.184539 x23 | .0446608 .2934171 0.15 0.879 -.5304262 .6197478 _cons | 5.578277 .2487596 22.42 0.000 5.090718 6.065837 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -28.878332 Iteration 1: log likelihood = -20.0781 Iteration 2: log likelihood = -19.991839 Iteration 3: log likelihood = -19.991746 Iteration 4: log likelihood = -19.991746 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .8403884061 (1/df) Deviance = .8403884 Pearson = .8280232353 (1/df) Pearson = .8280232 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.99174639 AIC = 7.426213 BIC = -1.105521743 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.280548 .255536 -8.92 0.000 -2.78139 -1.779707 x2 | -1.606535 .131908 -12.18 0.000 -1.86507 -1.348 x3 | .2267733 .1807611 1.25 0.210 -.1275119 .5810585 x12 | .6682655 .2625443 2.55 0.011 .1536881 1.182843 x13 | .1527163 .275426 0.55 0.579 -.3871087 .6925413 _cons | 5.613868 .1886307 29.76 0.000 5.244159 5.983578 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -32.635916 Iteration 1: log likelihood = -22.999352 Iteration 2: log likelihood = -22.82671 Iteration 3: log likelihood = -22.826408 Iteration 4: log likelihood = -22.826408 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 6.509711934 (1/df) Deviance = 6.509712 Pearson = 7.344109534 (1/df) Pearson = 7.34411 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.82640815 AIC = 8.236117 BIC = 4.563801785 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.810109 .3595733 -5.03 0.000 -2.514859 -1.105358 x2 | -1.203973 .3800585 -3.17 0.002 -1.948874 -.4590719 x3 | .6074439 .4068549 1.49 0.135 -.189977 1.404865 x13 | -.1702877 .3863394 -0.44 0.659 -.9274989 .5869235 x23 | -.2781541 .398183 -0.70 0.485 -1.058578 .5022702 _cons | 5.211306 .4032695 12.92 0.000 4.420912 6.0017 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 233.6785 .0197105 1.153373 7.76114 6.991104 3 .051215 | 2. | 2 256.6349 .0214818 -2.743451 1.123789 1.148369 2 .570128 | 3. | 3 236.6146 .0310198 3.085566 7.729618 6.977386 2 .0209669 | 4. | 4 212.7273 .0506216 2.806378 7.420065 6.698198 2 .0244767 | 5. | 5 264.6154 .0618813 -.8206921 1.102923 1.125218 1 .2936256 | |-------------------------------------------------------------------------------| 6. | 6 274.2029 .0355815 -1.105522 .8280233 .8403884 1 .3628448 | 7. | 7 183.3333 .1626263 4.563802 7.34411 6.509712 1 .0067283 | +-------------------------------------------------------------------------------+ nk is 564 Buen modelo: Sistema 4to solo < estimacion (9 < 257) BICs = -2.74 -1.11 -0.82 Estimacion subregistro 257 IC ( 177 ; 337) Estimacion del total 821 IC ( 741 ; 901) Estimacion BMA: 257 IC ( 149 ; 365) Total registros = 564 (note: file /tmp/model_info_ong_gov_jud_01_04.dta not found) (note: file /tmp/bma_info_ong_gov_jud_01_04.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_01_04.dta not found) . estimacion ong for jud _01_04 ong for jud cell values: 30 1 27 38 79 9 334 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -65.961745 Iteration 1: log likelihood = -51.937803 Iteration 2: log likelihood = -51.804315 Iteration 3: log likelihood = -51.804204 Iteration 4: log likelihood = -51.804204 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 67.83147823 (1/df) Deviance = 22.61049 Pearson = 68.12441099 (1/df) Pearson = 22.70814 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -51.80420392 AIC = 15.94406 BIC = 61.99374778 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.700552 .1176571 -14.45 0.000 -1.931156 -1.469948 x2 | -1.441047 .109816 -13.12 0.000 -1.656283 -1.225812 x3 | 1.13024 .1643458 6.88 0.000 .8081285 1.452352 _cons | 4.642362 .1717338 27.03 0.000 4.30577 4.978954 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -56.409032 Iteration 1: log likelihood = -45.988337 Iteration 2: log likelihood = -45.916745 Iteration 3: log likelihood = -45.916708 Iteration 4: log likelihood = -45.916708 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 56.05648661 (1/df) Deviance = 28.02824 Pearson = 55.21493561 (1/df) Pearson = 27.60747 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -45.91670811 AIC = 14.54763 BIC = 52.16466631 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.939035 .1424668 -13.61 0.000 -2.218264 -1.659805 x2 | -1.636085 .1275767 -12.82 0.000 -1.886131 -1.386039 x3 | 1.041454 .1678877 6.20 0.000 .7124001 1.370508 x12 | .895685 .2528211 3.54 0.000 .4001648 1.391205 _cons | 4.769687 .1765794 27.01 0.000 4.423598 5.115776 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -50.165012 Iteration 1: log likelihood = -38.03447 Iteration 2: log likelihood = -37.899152 Iteration 3: log likelihood = -37.89864 Iteration 4: log likelihood = -37.89864 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 40.02035078 (1/df) Deviance = 20.01018 Pearson = 42.23210785 (1/df) Pearson = 21.11605 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.8986402 AIC = 12.25675 BIC = 36.12853048 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.0656306 .3793131 -0.17 0.863 -.8090705 .6778094 x2 | -1.288481 .1076902 -11.96 0.000 -1.49955 -1.077412 x3 | 2.294255 .3473596 6.60 0.000 1.613443 2.975068 x13 | -1.914766 .404776 -4.73 0.000 -2.708112 -1.121419 _cons | 3.485706 .3502974 9.95 0.000 2.799135 4.172276 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -58.178235 Iteration 1: log likelihood = -32.326847 Iteration 2: log likelihood = -31.450071 Iteration 3: log likelihood = -31.446155 Iteration 4: log likelihood = -31.446155 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 27.11537943 (1/df) Deviance = 13.55769 Pearson = 31.70342013 (1/df) Pearson = 15.85171 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -31.44615452 AIC = 10.41319 BIC = 23.22355913 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.984562 .1400394 -14.17 0.000 -2.259035 -1.71009 x2 | -3.448344 .3761301 -9.17 0.000 -4.185545 -2.711143 x3 | .1379487 .2103424 0.66 0.512 -.2743148 .5502122 x23 | 2.250814 .3916864 5.75 0.000 1.483123 3.018505 _cons | 5.622148 .2143055 26.23 0.000 5.202117 6.042179 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -36.834104 Iteration 1: log likelihood = -19.494159 Iteration 2: log likelihood = -18.765716 Iteration 3: log likelihood = -18.759472 Iteration 4: log likelihood = -18.75947 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.74201113 (1/df) Deviance = 1.742011 Pearson = 1.459966064 (1/df) Pearson = 1.459966 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.75947037 AIC = 7.074134 BIC = -.203899019 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.515304 .2000776 -12.57 0.000 -2.907449 -2.123159 x2 | -4.152092 .4114694 -10.09 0.000 -4.958557 -3.345626 x3 | -.3417493 .2516999 -1.36 0.175 -.835072 .1515734 x12 | 1.471955 .2892279 5.09 0.000 .9050783 2.038831 x23 | 2.730512 .4153638 6.57 0.000 1.916414 3.54461 _cons | 6.15289 .2575788 23.89 0.000 5.648045 6.657735 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -44.716404 Iteration 1: log likelihood = -34.336069 Iteration 2: log likelihood = -34.196631 Iteration 3: log likelihood = -34.19608 Iteration 4: log likelihood = -34.19608 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 32.61522987 (1/df) Deviance = 32.61523 Pearson = 26.5489316 (1/df) Pearson = 26.54893 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -34.19607974 AIC = 11.48459 BIC = 30.66931972 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.365317 .3967018 -0.92 0.357 -1.142838 .4122043 x2 | -1.441693 .1251089 -11.52 0.000 -1.686902 -1.196484 x3 | 2.172223 .3518087 6.17 0.000 1.482691 2.861756 x12 | .7012931 .2515848 2.79 0.005 .2081959 1.19439 x13 | -1.792734 .4086003 -4.39 0.000 -2.593576 -.9918917 _cons | 3.638918 .3560384 10.22 0.000 2.941095 4.33674 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -50.204509 Iteration 1: log likelihood = -33.244869 Iteration 2: log likelihood = -31.532829 Iteration 3: log likelihood = -31.424868 Iteration 4: log likelihood = -31.42418 Iteration 5: log likelihood = -31.42418 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 27.07143012 (1/df) Deviance = 27.07143 Pearson = 31.56188495 (1/df) Pearson = 31.56188 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -31.42417987 AIC = 10.69262 BIC = 25.12551997 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.197225 1.054093 -2.08 0.037 -4.263208 -.1312411 x2 | -3.637586 1.013072 -3.59 0.000 -5.623172 -1.652001 x3 | -.0752179 1.067938 -0.07 0.944 -2.168337 2.017901 x13 | .2168282 1.063521 0.20 0.838 -1.867634 2.301291 x23 | 2.440056 1.018951 2.39 0.017 .4429497 4.437162 _cons | 5.834811 1.066502 5.47 0.000 3.744505 7.925117 ------------------------------------------------------------------------------ +------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |------------------------------------------------------------------------------| 1. | 1 103.7892 .0294925 61.99375 68.12441 67.83148 3 1.08e-14 | 2. | 2 117.8824 .0311803 52.16467 55.21494 56.05649 2 1.02e-12 | 3. | 3 32.64545 .1227083 36.12853 42.23211 40.02035 2 6.75e-10 | 4. | 4 276.4828 .0459268 23.22356 31.70342 27.11538 2 1.31e-07 | 5. | 5 470.0741 .0663468 -.203899 1.459966 1.742011 1 .2269355 | |------------------------------------------------------------------------------| 6. | 6 38.05063 .1267633 30.66932 26.54893 32.61523 1 2.57e-07 | 7. | 7 342 1.137427 25.12552 31.56189 27.07143 1 1.93e-08 | +------------------------------------------------------------------------------+ nk is 518 Buen modelo: Sistema 4to solo < estimacion (55 < 470) BICs = -0.20 23.22 25.13 Estimacion subregistro 470 IC ( 229 ; 711) Estimacion del total 988 IC ( 747 ; 1229) Estimacion BMA: 470 IC ( 229 ; 711) Total registros = 518 (note: file /tmp/model_info_ong_for_jud_01_04.dta not found) (note: file /tmp/bma_info_ong_for_jud_01_04.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_01_04.dta not found) . estimacion gov for jud _01_04 gov for jud cell values: 3 0 84 64 106 10 277 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -68.386651 Iteration 1: log likelihood = -62.376767 Iteration 2: log likelihood = -62.366108 Iteration 3: log likelihood = -62.366107 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 91.34781934 (1/df) Deviance = 30.44927 Pearson = 79.83890529 (1/df) Pearson = 26.61297 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -62.36610742 AIC = 18.96174 BIC = 85.51008889 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.244827 .1017219 -12.24 0.000 -1.444199 -1.045456 x2 | -1.542225 .1086987 -14.19 0.000 -1.75527 -1.329179 x3 | .8281503 .1372986 6.03 0.000 .55905 1.097251 _cons | 4.877657 .1461499 33.37 0.000 4.591208 5.164105 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -64.674886 Iteration 1: log likelihood = -41.524003 Iteration 2: log likelihood = -39.052816 Iteration 3: log likelihood = -38.991296 Iteration 4: log likelihood = -38.991211 Iteration 5: log likelihood = -38.991211 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 44.59802619 (1/df) Deviance = 22.29901 Pearson = 40.07388865 (1/df) Pearson = 20.03694 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -38.99121085 AIC = 12.56892 BIC = 40.70620589 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.9513637 .1086411 -8.76 0.000 -1.164296 -.738431 x2 | -1.194986 .1169052 -10.22 0.000 -1.424116 -.9658558 x3 | .9586251 .1367292 7.01 0.000 .6906409 1.226609 x12 | -2.703614 .5947747 -4.55 0.000 -3.869351 -1.537877 _cons | 4.665392 .1493485 31.24 0.000 4.372675 4.95811 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -55.240239 Iteration 1: log likelihood = -48.068773 Iteration 2: log likelihood = -47.959591 Iteration 3: log likelihood = -47.959194 Iteration 4: log likelihood = -47.959194 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 62.53399206 (1/df) Deviance = 31.267 Pearson = 44.2658091 (1/df) Pearson = 22.1329 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -47.95919378 AIC = 15.1312 BIC = 58.64217176 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .26725 .350609 0.76 0.446 -.4199309 .954431 x2 | -1.360741 .107365 -12.67 0.000 -1.571173 -1.15031 x3 | 2.056402 .3315307 6.20 0.000 1.406614 2.70619 x13 | -1.749377 .3701782 -4.73 0.000 -2.474913 -1.023841 _cons | 3.663326 .333957 10.97 0.000 3.008783 4.31787 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -53.963129 Iteration 1: log likelihood = -35.153001 Iteration 2: log likelihood = -34.29293 Iteration 3: log likelihood = -34.288323 Iteration 4: log likelihood = -34.288323 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 35.19225025 (1/df) Deviance = 17.59613 Pearson = 26.00785585 (1/df) Pearson = 13.00393 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -34.28832288 AIC = 11.22524 BIC = 31.30042995 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.507901 .1184854 -12.73 0.000 -1.740129 -1.275674 x2 | -3.564176 .3536042 -10.08 0.000 -4.257227 -2.871124 x3 | .0221169 .1667513 0.13 0.894 -.3047097 .3489435 x23 | 2.366646 .3701085 6.39 0.000 1.641247 3.092045 _cons | 5.666785 .1722318 32.90 0.000 5.329217 6.004353 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -54.778917 Iteration 1: log likelihood = -20.354139 Iteration 2: log likelihood = -17.067506 Iteration 3: log likelihood = -16.959587 Iteration 4: log likelihood = -16.959056 Iteration 5: log likelihood = -16.959056 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .5337166497 (1/df) Deviance = .5337166 Pearson = .2823473584 (1/df) Pearson = .2823474 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.95905608 AIC = 6.55973 BIC = -1.412193499 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.193201 .1245587 -9.58 0.000 -1.437331 -.9490701 x2 | -3.075032 .3624323 -8.48 0.000 -3.785386 -2.364678 x3 | .2719337 .165921 1.64 0.101 -.0532654 .5971328 x12 | -2.461777 .597887 -4.12 0.000 -3.633614 -1.28994 x23 | 2.116829 .3697351 5.73 0.000 1.392162 2.841497 _cons | 5.352084 .1764649 30.33 0.000 5.006219 5.697949 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -44.111463 Iteration 1: log likelihood = -20.510712 Iteration 2: log likelihood = -18.417836 Iteration 3: log likelihood = -18.368795 Iteration 4: log likelihood = -18.368641 Iteration 5: log likelihood = -18.368641 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 3.352887244 (1/df) Deviance = 3.352887 Pearson = 2.251630941 (1/df) Pearson = 2.251631 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.36864138 AIC = 6.962469 BIC = 1.406977095 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .875652 .3588918 2.44 0.015 .172237 1.579067 x2 | -.9605784 .1142106 -8.41 0.000 -1.184427 -.7367297 x3 | 2.360854 .330808 7.14 0.000 1.712482 3.009226 x12 | -2.938022 .5942509 -4.94 0.000 -4.102732 -1.773311 x13 | -2.053829 .3695311 -5.56 0.000 -2.778097 -1.329561 _cons | 3.263164 .3362203 9.71 0.000 2.604184 3.922143 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -52.41922 Iteration 1: log likelihood = -36.110507 Iteration 2: log likelihood = -33.013175 Iteration 3: log likelihood = -32.429328 Iteration 4: log likelihood = -32.304101 Iteration 5: log likelihood = -32.273244 Iteration 6: log likelihood = -32.266892 Iteration 7: log likelihood = -32.265537 Iteration 8: log likelihood = -32.26523 Iteration 9: log likelihood = -32.265154 Iteration 10: log likelihood = -32.265139 Iteration 11: log likelihood = -32.265137 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 31.14587822 (1/df) Deviance = 31.14588 Pearson = 23.36257506 (1/df) Pearson = 23.36258 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -32.26513686 AIC = 10.9329 BIC = 29.19996807 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -16.5177 1221.157 -0.01 0.989 -2409.941 2376.906 x2 | -18.37399 1221.157 -0.02 0.988 -2411.797 2375.049 x3 | -14.99242 1221.157 -0.01 0.990 -2408.416 2378.431 x13 | 15.03558 1221.157 0.01 0.990 -2378.388 2408.459 x23 | 17.17647 1221.157 0.01 0.989 -2376.247 2410.6 _cons | 20.67658 1221.157 0.02 0.986 -2372.747 2414.1 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 131.3226 .0213598 85.51009 79.83891 91.34782 3 3.32e-17 | 2. | 2 106.2073 .022305 40.70621 40.07389 44.59803 2 1.99e-09 | 3. | 3 38.99083 .1115273 58.64217 44.26581 62.53399 2 2.44e-10 | 4. | 4 289.1035 .0296638 31.30043 26.00786 35.19225 2 2.25e-06 | 5. | 5 211.0476 .0311399 -1.412194 .2823474 .5337167 1 .5951668 | |-------------------------------------------------------------------------------| 6. | 6 26.13208 .1130441 1.406977 2.251631 3.352887 1 .1334737 | 7. | 7 9.54e+08 1491224 29.19997 23.36258 31.14588 1 1.34e-06 | +-------------------------------------------------------------------------------+ nk is 544 Buen modelo: Sistema 4to solo < estimacion (29 < 211) BICs = -1.41 1.41 . Estimacion subregistro 211 IC ( 133 ; 289) Estimacion del total 755 IC ( 677 ; 833) Estimacion BMA: 140 IC ( -121 ; 401) Total registros = 544 (note: file /tmp/model_info_gov_for_jud_01_04.dta not found) (note: file /tmp/bma_info_gov_for_jud_01_04.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_01_04.dta not found) . . estimacion ong gov for _sy ong gov for cell values: 1 23 33 55 4 181 121 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -61.844492 Iteration 1: log likelihood = -50.115388 Iteration 2: log likelihood = -50.084195 Iteration 3: log likelihood = -50.084193 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 65.06200081 (1/df) Deviance = 21.68733 Pearson = 63.6015361 (1/df) Pearson = 21.20051 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -50.0841931 AIC = 15.45263 BIC = 59.22427036 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.225096 .1519525 -14.64 0.000 -2.522917 -1.927274 x2 | -1.503002 .1480233 -10.15 0.000 -1.793123 -1.212882 x3 | -1.828285 .1475788 -12.39 0.000 -2.117534 -1.539035 _cons | 6.593714 .1587405 41.54 0.000 6.282589 6.90484 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -60.901317 Iteration 1: log likelihood = -49.540328 Iteration 2: log likelihood = -49.477724 Iteration 3: log likelihood = -49.477718 Iteration 4: log likelihood = -49.477718 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 63.84905144 (1/df) Deviance = 31.92453 Pearson = 68.48431584 (1/df) Pearson = 34.24216 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -49.47771842 AIC = 15.56506 BIC = 59.95723114 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.3746 .2063417 -11.51 0.000 -2.779022 -1.970177 x2 | -1.631581 .19136 -8.53 0.000 -2.006639 -1.256522 x3 | -1.919242 .1737147 -11.05 0.000 -2.259716 -1.578767 x12 | .3322977 .2994144 1.11 0.267 -.2545436 .9191391 _cons | 6.715032 .1960644 34.25 0.000 6.330753 7.099312 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -44.081137 Iteration 1: log likelihood = -34.252089 Iteration 2: log likelihood = -34.115399 Iteration 3: log likelihood = -34.115373 Iteration 4: log likelihood = -34.115373 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 33.12436033 (1/df) Deviance = 16.56218 Pearson = 34.85555371 (1/df) Pearson = 17.42778 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -34.11537287 AIC = 11.17582 BIC = 29.23254003 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.977644 .2232493 -13.34 0.000 -3.415204 -2.540083 x2 | -2.01013 .2012436 -9.99 0.000 -2.40456 -1.6157 x3 | -2.506039 .2121786 -11.81 0.000 -2.921901 -2.090177 x13 | 1.675691 .2953845 5.67 0.000 1.096748 2.254634 _cons | 7.208627 .2145317 33.60 0.000 6.788152 7.629101 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -40.378617 Iteration 1: log likelihood = -21.908367 Iteration 2: log likelihood = -20.98894 Iteration 3: log likelihood = -20.981003 Iteration 4: log likelihood = -20.981002 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 6.855617823 (1/df) Deviance = 3.427809 Pearson = 6.906144574 (1/df) Pearson = 3.453072 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.98100161 AIC = 7.423143 BIC = 2.963797525 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.680534 .1442631 -11.65 0.000 -1.963284 -1.397783 x2 | -.5405648 .1946094 -2.78 0.005 -.9219922 -.1591373 x3 | -.8217321 .1986564 -4.14 0.000 -1.211092 -.4323728 x23 | -2.88695 .4943342 -5.84 0.000 -3.855827 -1.918073 _cons | 5.687867 .1974681 28.80 0.000 5.300837 6.074897 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -39.892819 Iteration 1: log likelihood = -18.512183 Iteration 2: log likelihood = -17.714071 Iteration 3: log likelihood = -17.707772 Iteration 4: log likelihood = -17.707771 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .3091557128 (1/df) Deviance = .3091557 Pearson = .3655468239 (1/df) Pearson = .3655468 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.70777056 AIC = 6.773649 BIC = -1.636754436 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.299283 .1963861 -6.62 0.000 -1.684193 -.9143733 x2 | -.1104746 .2495436 -0.44 0.658 -.599571 .3786218 x3 | -.5108256 .2201928 -2.32 0.020 -.9423955 -.0792558 x12 | -.743019 .2926424 -2.54 0.011 -1.316588 -.1694505 x23 | -3.197856 .5033753 -6.35 0.000 -4.184454 -2.211259 _cons | 5.306616 .2382211 22.28 0.000 4.839711 5.773521 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -32.749101 Iteration 1: log likelihood = -24.187124 Iteration 2: log likelihood = -23.932487 Iteration 3: log likelihood = -23.93103 Iteration 4: log likelihood = -23.93103 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 12.75567469 (1/df) Deviance = 12.75567 Pearson = 9.910507793 (1/df) Pearson = 9.910508 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.93103005 AIC = 8.551723 BIC = 10.80976454 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.492446 .528247 -8.50 0.000 -5.527791 -3.457101 x2 | -3.409496 .5081973 -6.71 0.000 -4.405544 -2.413448 x3 | -3.812203 .5054947 -7.54 0.000 -4.802954 -2.821451 x12 | 2.110213 .5579379 3.78 0.000 1.016675 3.203751 x13 | 2.981854 .5456713 5.46 0.000 1.912358 4.05135 _cons | 8.607993 .5136042 16.76 0.000 7.601347 9.614639 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -34.575757 Iteration 1: log likelihood = -18.739405 Iteration 2: log likelihood = -17.576637 Iteration 3: log likelihood = -17.556179 Iteration 4: log likelihood = -17.556179 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .005971619 (1/df) Deviance = .0059716 Pearson = .0058789914 (1/df) Pearson = .005879 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.55617851 AIC = 6.730337 BIC = -1.93993853 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.063003 .2213665 -9.32 0.000 -2.496873 -1.629132 x2 | -.871839 .2483145 -3.51 0.000 -1.358526 -.3851515 x3 | -1.273974 .2745708 -4.64 0.000 -1.812123 -.7358249 x13 | .7610496 .2939641 2.59 0.010 .1848906 1.337209 x23 | -2.555676 .5178355 -4.94 0.000 -3.570615 -1.540737 _cons | 6.070336 .2592006 23.42 0.000 5.562312 6.57836 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 730.4893 .0251985 59.22427 63.60154 65.062 3 9.99e-14 | 2. | 2 824.7105 .0384413 59.95723 68.48431 63.84905 2 1.35e-15 | 3. | 3 1351.036 .0460238 29.23254 34.85555 33.12436 2 2.70e-08 | 4. | 4 295.2632 .0389937 2.963798 6.906145 6.855618 2 .0316483 | 5. | 5 201.6667 .0567493 -1.636754 .3655468 .3091557 1 .5454417 | |-------------------------------------------------------------------------------| 6. | 6 5475.25 .2637893 10.80976 9.910508 12.75567 1 .0016434 | 7. | 7 432.8261 .0671849 -1.939939 .005879 .0059716 1 .9388824 | +-------------------------------------------------------------------------------+ nk is 418 Modelo aceptable: Sistema 4to solo < estimacion (507 < 656) BICs = -1.94 -1.64 2.96 Estimacion subregistro 433 IC ( 210 ; 656) Estimacion del total 851 IC ( 628 ; 1074) Estimacion BMA: 304 IC ( -57 ; 665) Total registros = 418 (note: file /tmp/model_info_ong_gov_for_sy.dta not found) (note: file /tmp/bma_info_ong_gov_for_sy.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_sy.dta not found) . estimacion ong gov jud _sy ong gov jud cell values: 16 8 49 39 101 84 616 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -54.356477 Iteration 1: log likelihood = -22.975843 Iteration 2: log likelihood = -22.790137 Iteration 3: log likelihood = -22.790041 Iteration 4: log likelihood = -22.790041 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 4.795529367 (1/df) Deviance = 1.59851 Pearson = 5.444778435 (1/df) Pearson = 1.814926 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.79004136 AIC = 7.654298 BIC = -1.04220108 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.422163 .1092122 -22.18 0.000 -2.636215 -2.208111 x2 | -1.718368 .0907304 -18.94 0.000 -1.896197 -1.54054 x3 | .2779028 .1139723 2.44 0.015 .0545212 .5012844 _cons | 6.13397 .1198154 51.20 0.000 5.899136 6.368804 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -45.502908 Iteration 1: log likelihood = -21.804905 Iteration 2: log likelihood = -21.036648 Iteration 3: log likelihood = -21.035974 Iteration 4: log likelihood = -21.035974 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.287395551 (1/df) Deviance = .6436978 Pearson = 1.25808496 (1/df) Pearson = .6290425 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.03597445 AIC = 7.43885 BIC = -2.604424747 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.527654 .1250764 -20.21 0.000 -2.7728 -2.282509 x2 | -1.784635 .0984168 -18.13 0.000 -1.977529 -1.591742 x3 | .2367905 .1168661 2.03 0.043 .0077371 .4658438 x12 | .4853525 .250432 1.94 0.053 -.0054852 .9761902 _cons | 6.186456 .1236166 50.05 0.000 5.944172 6.428741 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -56.159019 Iteration 1: log likelihood = -22.83234 Iteration 2: log likelihood = -22.744483 Iteration 3: log likelihood = -22.74447 Iteration 4: log likelihood = -22.74447 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 4.704387218 (1/df) Deviance = 2.352194 Pearson = 5.306516944 (1/df) Pearson = 2.653258 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.74447029 AIC = 7.926992 BIC = .8125669198 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.472576 .1999377 -12.37 0.000 -2.864446 -2.080705 x2 | -1.728465 .097059 -17.81 0.000 -1.918697 -1.538232 x3 | .2523526 .1417504 1.78 0.075 -.0254732 .5301784 x13 | .071887 .2382318 0.30 0.763 -.3950388 .5388128 _cons | 6.159281 .1460316 42.18 0.000 5.873065 6.445498 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -53.680205 Iteration 1: log likelihood = -22.915572 Iteration 2: log likelihood = -22.681235 Iteration 3: log likelihood = -22.681145 Iteration 4: log likelihood = -22.681145 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 4.577736717 (1/df) Deviance = 2.288868 Pearson = 5.185764428 (1/df) Pearson = 2.592882 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.68114504 AIC = 7.908899 BIC = .6859164189 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.395402 .1222582 -19.59 0.000 -2.635023 -2.15578 x2 | -1.624394 .2215063 -7.33 0.000 -2.058538 -1.19025 x3 | .3536045 .1992467 1.77 0.076 -.0369119 .7441208 x23 | -.1132191 .2431375 -0.47 0.641 -.5897598 .3633216 _cons | 6.058963 .2014649 30.07 0.000 5.664099 6.453827 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -45.500174 Iteration 1: log likelihood = -21.803198 Iteration 2: log likelihood = -21.035528 Iteration 3: log likelihood = -21.034852 Iteration 4: log likelihood = -21.034852 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.285149652 (1/df) Deviance = 1.28515 Pearson = 1.256374769 (1/df) Pearson = 1.256375 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.03485151 AIC = 7.724243 BIC = -.660760497 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.531427 .1484303 -17.05 0.000 -2.822345 -2.240509 x2 | -1.795178 .2432341 -7.38 0.000 -2.271908 -1.318448 x3 | .2282587 .2145907 1.06 0.287 -.1923315 .6488488 x12 | .4891247 .2628757 1.86 0.063 -.0261022 1.004351 x23 | .0121267 .2558628 0.05 0.962 -.4893551 .5136085 _cons | 6.194988 .2183405 28.37 0.000 5.767049 6.622928 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -45.515121 Iteration 1: log likelihood = -21.63547 Iteration 2: log likelihood = -20.868856 Iteration 3: log likelihood = -20.86816 Iteration 4: log likelihood = -20.86816 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .9517657096 (1/df) Deviance = .9517657 Pearson = .9343054108 (1/df) Pearson = .9343054 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.86815953 AIC = 7.676617 BIC = -.9941444395 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.629958 .2171193 -12.11 0.000 -3.055504 -2.204412 x2 | -1.808126 .1073516 -16.84 0.000 -2.018532 -1.597721 x3 | .1843037 .1476677 1.25 0.212 -.1051197 .4737271 x12 | .5088435 .2540761 2.00 0.045 .0108634 1.006823 x13 | .139936 .2417994 0.58 0.563 -.3339822 .6138541 _cons | 6.238943 .1530658 40.76 0.000 5.93894 6.538947 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -50.280911 Iteration 1: log likelihood = -23.370635 Iteration 2: log likelihood = -22.678874 Iteration 3: log likelihood = -22.673319 Iteration 4: log likelihood = -22.673317 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 4.562080693 (1/df) Deviance = 4.562081 Pearson = 5.192767161 (1/df) Pearson = 5.192767 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.67331703 AIC = 8.192376 BIC = 2.616170544 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.351375 .3700064 -6.35 0.000 -3.076574 -1.626176 x2 | -1.58412 .388125 -4.08 0.000 -2.344831 -.8234089 x3 | .3980715 .4051734 0.98 0.326 -.3960537 1.192197 x13 | -.0493135 .3920256 -0.13 0.900 -.8176695 .7190425 x23 | -.1534932 .4008638 -0.38 0.702 -.9391718 .6321853 _cons | 6.014937 .4031696 14.92 0.000 5.224739 6.805135 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 461.2637 .0143557 -1.042201 5.444778 4.795529 3 .141979 | 2. | 2 486.1205 .0152811 -2.604425 1.258085 1.287396 2 .533102 | 3. | 3 473.088 .0213252 .8125669 5.306517 4.704387 2 .0704214 | 4. | 4 427.9315 .0405881 .6859164 5.185764 4.577737 2 .0748041 | 5. | 5 490.2857 .0476726 -.6607605 1.256375 1.28515 1 .2623384 | |-------------------------------------------------------------------------------| 6. | 6 512.3168 .0234291 -.9941444 .9343054 .9517657 1 .3337467 | 7. | 7 409.4999 .1625458 2.616171 5.192767 4.562081 1 .0226811 | +-------------------------------------------------------------------------------+ nk is 913 Buen modelo: Sistema 4to solo < estimacion (12 < 486) BICs = -2.60 -1.04 -0.99 Estimacion subregistro 486 IC ( 361 ; 611) Estimacion del total 1399 IC ( 1274 ; 1524) Estimacion BMA: 479 IC ( 307 ; 651) Total registros = 913 (note: file /tmp/model_info_ong_gov_jud_sy.dta not found) (note: file /tmp/bma_info_ong_gov_jud_sy.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_sy.dta not found) . estimacion ong for jud _sy ong for jud cell values: 33 1 32 46 113 12 604 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -102.61571 Iteration 1: log likelihood = -64.06845 Iteration 2: log likelihood = -63.942216 Iteration 3: log likelihood = -63.942177 Iteration 4: log likelihood = -63.942177 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 90.42082019 (1/df) Deviance = 30.14027 Pearson = 93.73500466 (1/df) Pearson = 31.245 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -63.94217715 AIC = 19.41205 BIC = 84.58308975 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.091842 .1055557 -19.82 0.000 -2.298728 -1.884957 x2 | -1.688238 .0932079 -18.11 0.000 -1.870922 -1.505554 x3 | 1.193034 .147479 8.09 0.000 .9039808 1.482088 _cons | 5.182796 .1522825 34.03 0.000 4.884328 5.481264 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -81.11197 Iteration 1: log likelihood = -55.444929 Iteration 2: log likelihood = -54.892318 Iteration 3: log likelihood = -54.890582 Iteration 4: log likelihood = -54.890582 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 72.31762959 (1/df) Deviance = 36.15881 Pearson = 72.85525339 (1/df) Pearson = 36.42763 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -54.89058185 AIC = 17.11159 BIC = 68.42580929 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.333142 .1259949 -18.52 0.000 -2.580087 -2.086197 x2 | -1.861537 .1051389 -17.71 0.000 -2.067606 -1.655469 x3 | 1.104246 .1502236 7.35 0.000 .8098133 1.398679 x12 | 1.031189 .2308386 4.47 0.000 .5787533 1.483624 _cons | 5.299328 .1556366 34.05 0.000 4.994286 5.60437 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -77.219217 Iteration 1: log likelihood = -44.105574 Iteration 2: log likelihood = -43.868525 Iteration 3: log likelihood = -43.868413 Iteration 4: log likelihood = -43.868413 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 50.27329178 (1/df) Deviance = 25.13665 Pearson = 58.38884536 (1/df) Pearson = 29.19442 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -43.86841295 AIC = 13.9624 BIC = 46.38147148 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.3645466 .3319735 -1.10 0.272 -1.015203 .2861094 x2 | -1.534597 .090934 -16.88 0.000 -1.712824 -1.35637 x3 | 2.360382 .3005403 7.85 0.000 1.771333 2.94943 x13 | -2.036142 .3563505 -5.71 0.000 -2.734576 -1.337708 _cons | 4.019504 .3026588 13.28 0.000 3.426303 4.612704 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -98.650089 Iteration 1: log likelihood = -39.664414 Iteration 2: log likelihood = -37.784148 Iteration 3: log likelihood = -37.770008 Iteration 4: log likelihood = -37.770007 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 38.07648025 (1/df) Deviance = 19.03824 Pearson = 48.16500702 (1/df) Pearson = 24.0825 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.77000718 AIC = 12.22 BIC = 34.18465995 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.402019 .1285428 -18.69 0.000 -2.653958 -2.15008 x2 | -3.752379 .3354935 -11.18 0.000 -4.409934 -3.094824 x3 | .1378698 .1928863 0.71 0.475 -.2401803 .5159199 x23 | 2.280787 .3478182 6.56 0.000 1.599076 2.962499 _cons | 6.23066 .1956077 31.85 0.000 5.847276 6.614044 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -57.87221 Iteration 1: log likelihood = -21.669121 Iteration 2: log likelihood = -19.721446 Iteration 3: log likelihood = -19.699607 Iteration 4: log likelihood = -19.699605 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.935675154 (1/df) Deviance = 1.935675 Pearson = 1.578646173 (1/df) Pearson = 1.578646 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.69960463 AIC = 7.342744 BIC = -.0102349946 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.937838 .1813991 -16.20 0.000 -3.293374 -2.582303 x2 | -4.442121 .3650733 -12.17 0.000 -5.157651 -3.72659 x3 | -.3629055 .2301937 -1.58 0.115 -.8140768 .0882658 x12 | 1.635885 .2651743 6.17 0.000 1.116153 2.155617 x23 | 2.781563 .3698128 7.52 0.000 2.056743 3.506382 _cons | 6.76648 .2337622 28.95 0.000 6.308314 7.224645 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -62.87439 Iteration 1: log likelihood = -38.17208 Iteration 2: log likelihood = -37.598961 Iteration 3: log likelihood = -37.597787 Iteration 4: log likelihood = -37.597787 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 37.7320402 (1/df) Deviance = 37.73204 Pearson = 30.5260708 (1/df) Pearson = 30.52607 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.59778716 AIC = 12.45651 BIC = 35.78613005 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.6727355 .344974 -1.95 0.051 -1.348872 .0034011 x2 | -1.676186 .1024948 -16.35 0.000 -1.877073 -1.4753 x3 | 2.242481 .3036164 7.39 0.000 1.647404 2.837558 x12 | .8458381 .2296464 3.68 0.000 .3957394 1.295937 x13 | -1.918242 .3589486 -5.34 0.000 -2.621768 -1.214715 _cons | 4.161093 .3063307 13.58 0.000 3.560696 4.76149 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -77.641675 Iteration 1: log likelihood = -41.453422 Iteration 2: log likelihood = -38.102959 Iteration 3: log likelihood = -37.774493 Iteration 4: log likelihood = -37.766729 Iteration 5: log likelihood = -37.766708 Iteration 6: log likelihood = -37.766708 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 38.06988257 (1/df) Deviance = 38.06988 Pearson = 48.1051599 (1/df) Pearson = 48.10516 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.76670834 AIC = 12.50477 BIC = 36.12397242 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.484907 1.040833 -2.39 0.017 -4.524902 -.4449115 x2 | -3.828641 1.010811 -3.79 0.000 -5.809795 -1.847488 x3 | .0548716 1.052027 0.05 0.958 -2.007063 2.116807 x13 | .0842181 1.048863 0.08 0.936 -1.971515 2.139951 x23 | 2.357049 1.014968 2.32 0.020 .367748 4.346351 _cons | 6.313548 1.051224 6.01 0.000 4.253186 8.37391 ------------------------------------------------------------------------------ +------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |------------------------------------------------------------------------------| 1. | 1 178.1803 .02319 84.58309 93.73501 90.42082 3 3.45e-20 | 2. | 2 200.2022 .0242228 68.42581 72.85526 72.31763 2 1.51e-16 | 3. | 3 55.67347 .0916023 46.38147 58.38884 50.27329 2 2.09e-13 | 4. | 4 508.0909 .0382624 34.18466 48.16501 38.07648 2 3.48e-11 | 5. | 5 868.25 .0546448 -.010235 1.578646 1.935675 1 .2089558 | |------------------------------------------------------------------------------| 6. | 6 64.14159 .0938385 35.78613 30.52607 37.73204 1 3.29e-08 | 7. | 7 552 1.105072 36.12397 48.10516 38.06988 1 4.04e-12 | +------------------------------------------------------------------------------+ nk is 841 Buen modelo: Sistema 4to solo < estimacion (84 < 868) BICs = -0.01 34.18 35.79 Estimacion subregistro 868 IC ( 466 ; 1270) Estimacion del total 1709 IC ( 1307 ; 2111) Estimacion BMA: 868 IC ( 466 ; 1270) Total registros = 841 (note: file /tmp/model_info_ong_for_jud_sy.dta not found) (note: file /tmp/bma_info_ong_for_jud_sy.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_sy.dta not found) . estimacion gov for jud _sy gov for jud cell values: 5 0 112 92 141 13 524 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -87.506285 Iteration 1: log likelihood = -71.3661 Iteration 2: log likelihood = -71.319803 Iteration 3: log likelihood = -71.319798 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 106.9364994 (1/df) Deviance = 35.6455 Pearson = 94.51677803 (1/df) Pearson = 31.50559 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -71.31979764 AIC = 21.51994 BIC = 101.098769 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.486787 .0849271 -17.51 0.000 -1.653241 -1.320333 x2 | -1.812896 .0923424 -19.63 0.000 -1.993883 -1.631908 x3 | .799991 .1159344 6.90 0.000 .5727638 1.027218 _cons | 5.506869 .1220239 45.13 0.000 5.267706 5.746031 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -94.245152 Iteration 1: log likelihood = -55.994018 Iteration 2: log likelihood = -51.293282 Iteration 3: log likelihood = -51.199307 Iteration 4: log likelihood = -51.199096 Iteration 5: log likelihood = -51.199096 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 66.69509702 (1/df) Deviance = 33.34755 Pearson = 59.41861535 (1/df) Pearson = 29.70931 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -51.19909643 AIC = 16.05688 BIC = 62.80327672 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.284815 .089059 -14.43 0.000 -1.459367 -1.110263 x2 | -1.565982 .0975861 -16.05 0.000 -1.757248 -1.374717 x3 | .8989992 .1157574 7.77 0.000 .6721189 1.12588 x12 | -2.1427 .4630605 -4.63 0.000 -3.050282 -1.235118 _cons | 5.362492 .1237262 43.34 0.000 5.119994 5.604991 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -65.062052 Iteration 1: log likelihood = -48.302145 Iteration 2: log likelihood = -47.961518 Iteration 3: log likelihood = -47.961184 Iteration 4: log likelihood = -47.961184 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 60.21927267 (1/df) Deviance = 30.10964 Pearson = 41.1152534 (1/df) Pearson = 20.55763 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -47.96118425 AIC = 15.13177 BIC = 56.32745237 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .1673655 .3057741 0.55 0.584 -.4319407 .7666717 x2 | -1.606694 .0906804 -17.72 0.000 -1.784425 -1.428964 x3 | 2.145364 .2900551 7.40 0.000 1.576866 2.713862 x13 | -1.904979 .3217896 -5.92 0.000 -2.535675 -1.274283 _cons | 4.171644 .2917979 14.30 0.000 3.59973 4.743557 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -73.832593 Iteration 1: log likelihood = -34.484053 Iteration 2: log likelihood = -32.210182 Iteration 3: log likelihood = -32.180318 Iteration 4: log likelihood = -32.180315 Iteration 5: log likelihood = -32.180315 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 28.65753355 (1/df) Deviance = 14.32877 Pearson = 21.31795962 (1/df) Pearson = 10.65898 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -32.1803147 AIC = 10.62295 BIC = 24.76571325 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.756973 .1001096 -17.55 0.000 -1.953185 -1.560762 x2 | -3.873007 .3083534 -12.56 0.000 -4.477369 -3.268646 x3 | .0172418 .1404672 0.12 0.902 -.2580689 .2925525 x23 | 2.401415 .3217195 7.46 0.000 1.770857 3.031974 _cons | 6.278762 .1445389 43.44 0.000 5.995471 6.562053 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -80.595086 Iteration 1: log likelihood = -24.305107 Iteration 2: log likelihood = -18.485286 Iteration 3: log likelihood = -18.286052 Iteration 4: log likelihood = -18.285193 Iteration 5: log likelihood = -18.285193 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .8672903104 (1/df) Deviance = .8672903 Pearson = .4596601965 (1/df) Pearson = .4596602 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.28519307 AIC = 6.938627 BIC = -1.078619839 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.542993 .1041008 -14.82 0.000 -1.747027 -1.338959 x2 | -3.531784 .3143784 -11.23 0.000 -4.147954 -2.915613 x3 | .1967103 .1407059 1.40 0.162 -.0790681 .4724887 x12 | -1.884522 .4661872 -4.04 0.000 -2.798232 -.9708118 x23 | 2.221947 .3218238 6.90 0.000 1.591184 2.85271 _cons | 6.064781 .1473314 41.16 0.000 5.776017 6.353546 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -65.609241 Iteration 1: log likelihood = -24.88118 Iteration 2: log likelihood = -20.873928 Iteration 3: log likelihood = -20.800723 Iteration 4: log likelihood = -20.800451 Iteration 5: log likelihood = -20.800451 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 5.89780626 (1/df) Deviance = 5.897806 Pearson = 4.027987262 (1/df) Pearson = 4.027987 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.80045105 AIC = 7.657272 BIC = 3.951896111 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .6198932 .3113045 1.99 0.046 .0097475 1.230039 x2 | -1.312732 .0948715 -13.84 0.000 -1.498676 -1.126787 x3 | 2.383811 .2898539 8.22 0.000 1.815707 2.951914 x12 | -2.39595 .462496 -5.18 0.000 -3.302426 -1.489475 x13 | -2.143425 .3216082 -6.66 0.000 -2.773766 -1.513085 _cons | 3.877681 .2931274 13.23 0.000 3.303162 4.4522 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -65.824224 Iteration 1: log likelihood = -36.483851 Iteration 2: log likelihood = -31.410139 Iteration 3: log likelihood = -30.372675 Iteration 4: log likelihood = -30.157578 Iteration 5: log likelihood = -30.106829 Iteration 6: log likelihood = -30.095189 Iteration 7: log likelihood = -30.092707 Iteration 8: log likelihood = -30.092278 Iteration 9: log likelihood = -30.092232 Iteration 10: log likelihood = -30.092222 Iteration 11: log likelihood = -30.09222 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 24.48134503 (1/df) Deviance = 24.48135 Pearson = 18.78021458 (1/df) Pearson = 18.78021 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -30.09222043 AIC = 10.31206 BIC = 22.53543488 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -17.04119 1391.456 -0.01 0.990 -2744.246 2710.163 x2 | -18.998 1391.456 -0.01 0.989 -2746.203 2708.207 x3 | -15.26986 1391.456 -0.01 0.991 -2742.474 2711.935 x13 | 15.30357 1391.456 0.01 0.991 -2711.901 2742.508 x23 | 17.52641 1391.456 0.01 0.990 -2709.678 2744.731 _cons | 21.56297 1391.456 0.02 0.988 -2705.642 2748.768 ------------------------------------------------------------------------------ +------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |------------------------------------------------------------------------------| 1. | 1 246.3784 .0148898 101.0988 94.51678 106.9365 3 2.35e-20 | 2. | 2 213.2558 .0153082 62.80328 59.41862 66.6951 2 1.25e-13 | 3. | 3 64.82191 .085146 56.32745 41.11525 60.21927 2 1.18e-09 | 4. | 4 533.1282 .0208915 24.76571 21.31796 28.65753 2 .0000235 | 5. | 5 430.4286 .0217065 -1.07862 .4596602 .8672903 1 .4977828 | |------------------------------------------------------------------------------| 6. | 6 48.31206 .0859237 3.951896 4.027987 5.897806 1 .0447513 | 7. | 7 2.32e+09 1936151 22.53543 18.78021 24.48134 1 .0000147 | +------------------------------------------------------------------------------+ nk is 887 Buen modelo: Sistema 4to solo < estimacion (38 < 430) BICs = -1.08 3.95 . Estimacion subregistro 430 IC ( 299 ; 561) Estimacion del total 1317 IC ( 1186 ; 1448) Estimacion BMA: 365 IC ( -37 ; 767) Total registros = 887 (note: file /tmp/model_info_gov_for_jud_sy.dta not found) (note: file /tmp/bma_info_gov_for_jud_sy.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_sy.dta not found) . . estimacion ong gov for _tay ong gov for cell values: 0 19 6 11 4 131 68 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -47.192013 Iteration 1: log likelihood = -35.728978 Iteration 2: log likelihood = -35.656962 Iteration 3: log likelihood = -35.656883 Iteration 4: log likelihood = -35.656883 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 42.57443449 (1/df) Deviance = 14.19148 Pearson = 41.98185439 (1/df) Pearson = 13.99395 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -35.65688265 AIC = 11.33054 BIC = 36.73670405 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.884626 .2347293 -12.29 0.000 -3.344687 -2.424565 x2 | -1.228895 .2168224 -5.67 0.000 -1.653859 -.8039311 x3 | -2.044026 .2096854 -9.75 0.000 -2.455002 -1.63305 _cons | 6.089697 .2294826 26.54 0.000 5.639919 6.539474 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -37.784167 Iteration 1: log likelihood = -23.793623 Iteration 2: log likelihood = -23.409272 Iteration 3: log likelihood = -23.40889 Iteration 4: log likelihood = -23.40889 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 18.07844826 (1/df) Deviance = 9.039224 Pearson = 29.99237121 (1/df) Pearson = 14.99619 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.40888954 AIC = 8.116826 BIC = 14.18662796 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.225373 .4094893 -10.32 0.000 -5.027957 -3.422789 x2 | -2.153311 .340977 -6.32 0.000 -2.821614 -1.485009 x3 | -2.778819 .3259006 -8.53 0.000 -3.417573 -2.140066 x12 | 2.264537 .4772006 4.75 0.000 1.329241 3.199833 _cons | 6.998327 .3477313 20.13 0.000 6.316786 7.679868 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -44.08776 Iteration 1: log likelihood = -35.358093 Iteration 2: log likelihood = -35.1358 Iteration 3: log likelihood = -35.135122 Iteration 4: log likelihood = -35.135122 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 41.53091349 (1/df) Deviance = 20.76546 Pearson = 43.89606137 (1/df) Pearson = 21.94803 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -35.13512215 AIC = 11.46718 BIC = 37.63909319 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.020637 .2742005 -11.02 0.000 -3.55806 -2.483214 x2 | -1.307157 .2350382 -5.56 0.000 -1.767823 -.8464906 x3 | -2.145168 .2360963 -9.09 0.000 -2.607909 -1.682428 x13 | .5357303 .5057089 1.06 0.289 -.4554409 1.526902 _cons | 6.182354 .250752 24.66 0.000 5.69089 6.673819 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -27.722995 Iteration 1: log likelihood = -15.997525 Iteration 2: log likelihood = -15.39095 Iteration 3: log likelihood = -15.383924 Iteration 4: log likelihood = -15.383919 Iteration 5: log likelihood = -15.383919 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 2.028507467 (1/df) Deviance = 1.014254 Pearson = 1.5561919 (1/df) Pearson = .7780959 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -15.38391914 AIC = 5.823977 BIC = -1.863312831 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.09433 .2119578 -9.88 0.000 -2.50976 -1.678901 x2 | .4022702 .3649518 1.10 0.270 -.3130221 1.117563 x3 | -.3043 .3742147 -0.81 0.416 -1.037747 .4291473 x23 | -3.320041 .6298439 -5.27 0.000 -4.554512 -2.08557 _cons | 4.492225 .3685583 12.19 0.000 3.769864 5.214586 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -26.907065 Iteration 1: log likelihood = -15.442598 Iteration 2: log likelihood = -14.908903 Iteration 3: log likelihood = -14.903829 Iteration 4: log likelihood = -14.903826 Iteration 5: log likelihood = -14.903826 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.068321815 (1/df) Deviance = 1.068322 Pearson = .5779753086 (1/df) Pearson = .5779753 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -14.90382631 AIC = 5.972522 BIC = -.8775883342 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.427748 .4258786 -5.70 0.000 -3.262455 -1.593042 x2 | .053314 .52902 0.10 0.920 -.9835463 1.090174 x3 | -.6061358 .5075192 -1.19 0.232 -1.600855 .3885836 x12 | .4669124 .4913365 0.95 0.342 -.4960894 1.429914 x23 | -3.018205 .717107 -4.21 0.000 -4.423709 -1.612701 _cons | 4.825644 .5218061 9.25 0.000 3.802922 5.848365 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -26.662675 Iteration 1: log likelihood = -19.714052 Iteration 2: log likelihood = -19.552858 Iteration 3: log likelihood = -19.552647 Iteration 4: log likelihood = -19.552647 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 10.36596297 (1/df) Deviance = 10.36596 Pearson = 8.047058821 (1/df) Pearson = 8.047059 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.55264689 AIC = 7.300756 BIC = 8.420052824 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -5.057519 .5802746 -8.72 0.000 -6.194836 -3.920202 x2 | -2.833213 .5144958 -5.51 0.000 -3.841606 -1.82482 x3 | -3.488903 .5075762 -6.87 0.000 -4.483734 -2.494072 x12 | 2.944439 .6133196 4.80 0.000 1.742355 4.146523 x13 | 1.879465 .6764862 2.78 0.005 .5535764 3.205354 _cons | 7.708411 .5218615 14.77 0.000 6.685581 8.73124 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -28.852828 Iteration 1: log likelihood = -15.601839 Iteration 2: log likelihood = -14.707358 Iteration 3: log likelihood = -14.698704 Iteration 4: log likelihood = -14.6987 Iteration 5: log likelihood = -14.6987 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .658069935 (1/df) Deviance = .6580699 Pearson = .3513513514 (1/df) Pearson = .3513514 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -14.69870037 AIC = 5.913914 BIC = -1.287840214 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.930758 .2454896 -7.86 0.000 -2.411909 -1.449607 x2 | .5465437 .3788676 1.44 0.149 -.1960232 1.289111 x3 | -.1046312 .4071316 -0.26 0.797 -.9025945 .6933321 x13 | -.5541483 .4907349 -1.13 0.259 -1.515971 .4076744 x23 | -3.464314 .638008 -5.43 0.000 -4.714787 -2.213842 _cons | 4.328654 .3888113 11.13 0.000 3.566597 5.09071 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 441.2876 .0526622 36.73671 41.98185 42.57444 3 4.05e-09 | 2. | 2 1094.8 .1209171 14.18663 29.99237 18.07845 2 3.07e-07 | 3. | 3 484.1304 .0628766 37.63909 43.89606 41.53091 2 2.94e-10 | 4. | 4 89.32 .1358352 -1.863313 1.556192 2.028507 2 .4592797 | 5. | 5 124.6667 .2722816 -.8775883 .5779753 1.068322 1 .447107 | |-------------------------------------------------------------------------------| 6. | 6 2227 .2723395 8.420053 8.047059 10.36596 1 .0045578 | 7. | 7 75.8421 .1511743 -1.28784 .3513514 .6580699 1 .5533491 | +-------------------------------------------------------------------------------+ nk is 239 Mal modelo: Sistema 4to solo > estimacion (394 > 156) (note: file /tmp/model_info_ong_gov_for_tay.dta not found) (note: file /tmp/bma_info_ong_gov_for_tay.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_tay.dta not found) . estimacion ong gov jud _tay ong gov jud cell values: 12 7 10 7 66 69 456 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -75.824683 Iteration 1: log likelihood = -34.417711 Iteration 2: log likelihood = -33.554153 Iteration 3: log likelihood = -33.551086 Iteration 4: log likelihood = -33.551086 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 30.92802637 (1/df) Deviance = 10.30934 Pearson = 43.18101048 (1/df) Pearson = 14.39367 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -33.55108588 AIC = 10.72888 BIC = 25.09029592 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.276414 .1806158 -18.14 0.000 -3.630415 -2.922414 x2 | -1.690842 .1124011 -15.04 0.000 -1.911144 -1.47054 x3 | .2001936 .1465531 1.37 0.172 -.0870451 .4874323 _cons | 5.892482 .1533499 38.43 0.000 5.591921 6.193042 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -44.282277 Iteration 1: log likelihood = -19.509595 Iteration 2: log likelihood = -18.979499 Iteration 3: log likelihood = -18.979103 Iteration 4: log likelihood = -18.979103 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.784060207 (1/df) Deviance = .8920301 Pearson = 1.76717995 (1/df) Pearson = .88359 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.97910279 AIC = 6.851172 BIC = -2.107760091 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.953606 .2579384 -15.33 0.000 -4.459156 -3.448056 x2 | -1.881545 .1229474 -15.30 0.000 -2.122517 -1.640572 x3 | .0584962 .1530092 0.38 0.702 -.2413964 .3583888 x12 | 1.99277 .3557684 5.60 0.000 1.295477 2.690064 _cons | 6.063997 .160015 37.90 0.000 5.750373 6.37762 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -78.339625 Iteration 1: log likelihood = -33.978303 Iteration 2: log likelihood = -33.212068 Iteration 3: log likelihood = -33.211088 Iteration 4: log likelihood = -33.211088 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 30.24802983 (1/df) Deviance = 15.12401 Pearson = 42.14704154 (1/df) Pearson = 21.07352 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -33.2110876 AIC = 10.91745 BIC = 26.35620953 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.476757 .3096674 -11.23 0.000 -4.083694 -2.86982 x2 | -1.716444 .1178086 -14.57 0.000 -1.947345 -1.485543 x3 | .1418534 .1624225 0.87 0.382 -.1764888 .4601955 x13 | .3101317 .3785025 0.82 0.413 -.4317196 1.051983 _cons | 5.950551 .1684388 35.33 0.000 5.620417 6.280685 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -68.457712 Iteration 1: log likelihood = -30.88862 Iteration 2: log likelihood = -30.054648 Iteration 3: log likelihood = -30.052161 Iteration 4: log likelihood = -30.052161 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 23.93017576 (1/df) Deviance = 11.96509 Pearson = 30.25658737 (1/df) Pearson = 15.12829 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -30.05216057 AIC = 10.0149 BIC = 20.03835547 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.01452 .1901968 -15.85 0.000 -3.387299 -2.641741 x2 | -.6776005 .4346089 -1.56 0.119 -1.529418 .1742173 x3 | 1.135852 .4217498 2.69 0.007 .3092374 1.962466 x23 | -1.109876 .451499 -2.46 0.014 -1.994798 -.2249546 _cons | 4.96043 .4231217 11.72 0.000 4.131127 5.789734 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -44.235533 Iteration 1: log likelihood = -19.340662 Iteration 2: log likelihood = -18.774017 Iteration 3: log likelihood = -18.773128 Iteration 4: log likelihood = -18.773128 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.372110733 (1/df) Deviance = 1.372111 Pearson = 1.356710941 (1/df) Pearson = 1.356711 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.77312806 AIC = 7.078037 BIC = -.5737994159 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.819908 .3196764 -11.95 0.000 -4.446462 -3.193354 x2 | -1.566762 .5090402 -3.08 0.002 -2.564463 -.5690619 x3 | .3566749 .4928054 0.72 0.469 -.6092059 1.322556 x12 | 1.859072 .4027803 4.62 0.000 1.069637 2.648507 x23 | -.3306995 .5184935 -0.64 0.524 -1.346928 .6855292 _cons | 5.765818 .4950254 11.65 0.000 4.795586 6.73605 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -43.954229 Iteration 1: log likelihood = -18.763359 Iteration 2: log likelihood = -18.124107 Iteration 3: log likelihood = -18.122523 Iteration 4: log likelihood = -18.122523 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .070901114 (1/df) Deviance = .0709011 Pearson = .0709259778 (1/df) Pearson = .070926 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.12252325 AIC = 6.892149 BIC = -1.875009035 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.278193 .3664854 -11.67 0.000 -4.996491 -3.559895 x2 | -1.932838 .1316985 -14.68 0.000 -2.190962 -1.674714 x3 | -.0444518 .1721751 -0.26 0.796 -.3819088 .2930053 x12 | 2.044064 .3588866 5.70 0.000 1.340659 2.747469 x13 | .4964369 .382789 1.30 0.195 -.2538157 1.246689 _cons | 6.166945 .17843 34.56 0.000 5.817228 6.516661 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -62.268271 Iteration 1: log likelihood = -28.959084 Iteration 2: log likelihood = -28.417081 Iteration 3: log likelihood = -28.414919 Iteration 4: log likelihood = -28.414919 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 20.65569259 (1/df) Deviance = 20.65569 Pearson = 30.17702576 (1/df) Pearson = 30.17703 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -28.41491899 AIC = 9.832834 BIC = 18.70978244 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.288196 .3966735 -5.77 0.000 -3.065662 -1.510731 x2 | -6.52e-18 .5345225 -0.00 1.000 -1.047645 1.047645 x3 | 1.868797 .5499368 3.40 0.001 .7909412 2.946654 x13 | -.8784288 .4524601 -1.94 0.052 -1.765234 .0083767 x23 | -1.787477 .5483436 -3.26 0.001 -2.862211 -.7127431 _cons | 4.234107 .5479115 7.73 0.000 3.16022 5.307993 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 362.3033 .0235162 25.0903 43.18101 30.92803 3 2.25e-09 | 2. | 2 430.0909 .0256048 -2.10776 1.76718 1.78406 2 .4132965 | 3. | 3 383.9647 .0283716 26.35621 42.14704 30.24803 2 7.05e-10 | 4. | 4 142.6552 .179032 20.03835 30.25659 23.93018 2 2.69e-07 | 5. | 5 319.2 .2450501 -.5737994 1.356711 1.372111 1 .2441083 | |-------------------------------------------------------------------------------| 6. | 6 476.7273 .0318373 -1.875009 .070926 .0709011 1 .7899933 | 7. | 7 69 .300207 18.70978 30.17702 20.65569 1 3.94e-08 | +-------------------------------------------------------------------------------+ nk is 627 Buen modelo: Sistema 4to solo < estimacion (6 < 430) BICs = -2.11 -1.88 -0.57 Estimacion subregistro 430 IC ( 289 ; 571) Estimacion del total 1057 IC ( 916 ; 1198) Estimacion BMA: 422 IC ( 167 ; 677) Total registros = 627 (note: file /tmp/model_info_ong_gov_jud_tay.dta not found) (note: file /tmp/bma_info_ong_gov_jud_tay.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_tay.dta not found) . estimacion ong for jud _tay ong for jud cell values: 5 1 17 13 66 6 456 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -76.661157 Iteration 1: log likelihood = -25.776325 Iteration 2: log likelihood = -24.405329 Iteration 3: log likelihood = -24.390079 Iteration 4: log likelihood = -24.390077 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 16.55483088 (1/df) Deviance = 5.518277 Pearson = 17.51030963 (1/df) Pearson = 5.83677 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -24.39007703 AIC = 8.111451 BIC = 10.71710043 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.86091 .1767621 -16.19 0.000 -3.207357 -2.514462 x2 | -2.018634 .1291491 -15.63 0.000 -2.271762 -1.765507 x3 | 1.501615 .2463837 6.09 0.000 1.018712 1.984518 _cons | 4.61697 .2504359 18.44 0.000 4.126125 5.107815 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -61.706661 Iteration 1: log likelihood = -26.494299 Iteration 2: log likelihood = -24.035015 Iteration 3: log likelihood = -23.980178 Iteration 4: log likelihood = -23.980038 Iteration 5: log likelihood = -23.980038 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 15.73475279 (1/df) Deviance = 7.867376 Pearson = 17.20636364 (1/df) Pearson = 8.603182 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -23.98003799 AIC = 8.280011 BIC = 11.8429325 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.92609 .1939863 -15.08 0.000 -3.306296 -2.545884 x2 | -2.050621 .1348564 -15.21 0.000 -2.314935 -1.786307 x3 | 1.481605 .2477168 5.98 0.000 .9960884 1.967121 x12 | .4411832 .4671041 0.94 0.345 -.474324 1.35669 _cons | 4.640888 .2521044 18.41 0.000 4.146773 5.135004 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -61.927283 Iteration 1: log likelihood = -17.829947 Iteration 2: log likelihood = -17.166813 Iteration 3: log likelihood = -17.165486 Iteration 4: log likelihood = -17.165486 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 2.105647862 (1/df) Deviance = 1.052824 Pearson = 2.333959463 (1/df) Pearson = 1.16698 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.16548552 AIC = 6.332996 BIC = -1.786172436 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.200395 .500192 -2.40 0.016 -2.180753 -.2200367 x2 | -1.909543 .1262795 -15.12 0.000 -2.157046 -1.662039 x3 | 2.418215 .4250637 5.69 0.000 1.585106 3.251325 x13 | -1.96623 .5454927 -3.60 0.000 -3.035376 -.8970841 _cons | 3.701302 .4273326 8.66 0.000 2.863745 4.538859 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -79.617517 Iteration 1: log likelihood = -18.864746 Iteration 2: log likelihood = -17.515894 Iteration 3: log likelihood = -17.491901 Iteration 4: log likelihood = -17.491883 Iteration 5: log likelihood = -17.491883 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 2.758442698 (1/df) Deviance = 1.379221 Pearson = 3.647284042 (1/df) Pearson = 1.823642 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.49188294 AIC = 6.426252 BIC = -1.133377601 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.133602 .2130075 -14.71 0.000 -3.551089 -2.716115 x2 | -3.79528 .5113156 -7.42 0.000 -4.79744 -2.79312 x3 | .4179054 .3474199 1.20 0.229 -.2630251 1.098836 x23 | 1.898864 .5269177 3.60 0.000 .8661246 2.931604 _cons | 5.698551 .3497074 16.30 0.000 5.013137 6.383965 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -57.551385 Iteration 1: log likelihood = -20.070145 Iteration 2: log likelihood = -16.39284 Iteration 3: log likelihood = -16.308757 Iteration 4: log likelihood = -16.30875 Iteration 5: log likelihood = -16.30875 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .3921762705 (1/df) Deviance = .3921763 Pearson = .4708249497 (1/df) Pearson = .4708249 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.30874973 AIC = 6.373928 BIC = -1.553733879 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.289279 .2470152 -13.32 0.000 -3.77342 -2.805139 x2 | -3.988361 .5309097 -7.51 0.000 -5.028925 -2.947797 x3 | .268264 .3684381 0.73 0.467 -.4538613 .9903893 x12 | .8043728 .4914998 1.64 0.102 -.1589491 1.767695 x23 | 2.048506 .5410067 3.79 0.000 .9881521 3.108859 _cons | 5.854229 .3714022 15.76 0.000 5.126294 6.582164 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -50.704791 Iteration 1: log likelihood = -18.869637 Iteration 2: log likelihood = -16.975347 Iteration 3: log likelihood = -16.939331 Iteration 4: log likelihood = -16.939287 Iteration 5: log likelihood = -16.939287 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.653249978 (1/df) Deviance = 1.65325 Pearson = 1.496103896 (1/df) Pearson = 1.496104 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.93928658 AIC = 6.554082 BIC = -.2926601714 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.267862 .510877 -2.48 0.013 -2.269162 -.2665613 x2 | -1.932838 .1316985 -14.68 0.000 -2.190962 -1.674714 x3 | 2.397895 .4264014 5.62 0.000 1.562164 3.233627 x12 | .3234002 .4662022 0.69 0.488 -.5903394 1.23714 x13 | -1.94591 .5465357 -3.56 0.000 -3.0171 -.8747198 _cons | 3.724598 .4289652 8.68 0.000 2.883841 4.565354 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -55.401724 Iteration 1: log likelihood = -19.14928 Iteration 2: log likelihood = -17.058197 Iteration 3: log likelihood = -16.92021 Iteration 4: log likelihood = -16.918998 Iteration 5: log likelihood = -16.918998 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.612671876 (1/df) Deviance = 1.612672 Pearson = 1.891486932 (1/df) Pearson = 1.891487 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.91899753 AIC = 6.548285 BIC = -.3332382734 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.791759 1.080123 -1.66 0.097 -3.908762 .3252436 x2 | -2.564949 1.037749 -2.47 0.013 -4.5989 -.5309986 x3 | 1.761105 1.116146 1.58 0.115 -.4265005 3.94871 x13 | -1.374866 1.101833 -1.25 0.212 -3.53442 .784688 x23 | .6685338 1.045525 0.64 0.523 -1.380657 2.717724 _cons | 4.356709 1.115164 3.91 0.000 2.171028 6.542389 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 101.187 .0627182 10.7171 17.51031 16.55483 3 .0005549 | 2. | 2 103.6364 .0635566 11.84293 17.20636 15.73475 2 .0001835 | 3. | 3 40.5 .1826132 -1.786172 2.33396 2.105648 2 .3113058 | 4. | 4 298.4348 .1222953 -1.133378 3.647284 2.758443 2 .1614367 | 5. | 5 348.7059 .1379396 -1.553734 .470825 .3921763 1 .4926079 | |-------------------------------------------------------------------------------| 6. | 6 41.45454 .1840112 -.2926602 1.496104 1.65325 1 .2212718 | 7. | 7 77.99999 1.24359 -.3332383 1.891487 1.612672 1 .1690343 | +-------------------------------------------------------------------------------+ nk is 564 Modelo aceptable: Sistema 4to solo < estimacion (69 < 75) BICs = -1.79 -1.55 -1.13 Estimacion subregistro 40 IC ( 5 ; 75) Estimacion del total 604 IC ( 569 ; 639) Estimacion BMA: 114 IC ( -300 ; 528) Total registros = 564 (note: file /tmp/model_info_ong_for_jud_tay.dta not found) (note: file /tmp/bma_info_ong_for_jud_tay.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_tay.dta not found) . estimacion gov for jud _tay gov for jud cell values: 4 0 74 76 67 7 399 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -62.23443 Iteration 1: log likelihood = -45.213534 Iteration 2: log likelihood = -45.037843 Iteration 3: log likelihood = -45.037685 Iteration 4: log likelihood = -45.037685 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 56.8147256 (1/df) Deviance = 18.93824 Pearson = 50.28600243 (1/df) Pearson = 16.762 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -45.0376851 AIC = 14.01077 BIC = 50.97699515 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.547401 .1042357 -14.85 0.000 -1.751699 -1.343103 x2 | -2.327507 .1285282 -18.11 0.000 -2.579417 -2.075596 x3 | .4887572 .1374688 3.56 0.000 .2193233 .758191 _cons | 5.52419 .1446623 38.19 0.000 5.240657 5.807723 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -67.549182 Iteration 1: log likelihood = -42.065932 Iteration 2: log likelihood = -39.096405 Iteration 3: log likelihood = -39.007272 Iteration 4: log likelihood = -39.007179 Iteration 5: log likelihood = -39.007179 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 44.75371319 (1/df) Deviance = 22.37686 Pearson = 38.67556276 (1/df) Pearson = 19.33778 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -39.0071789 AIC = 12.57348 BIC = 40.86189289 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.430938 .1080903 -13.24 0.000 -1.642791 -1.219085 x2 | -2.137508 .1361263 -15.70 0.000 -2.404311 -1.870706 x3 | .5578931 .1376399 4.05 0.000 .2881239 .8276624 x12 | -1.486833 .5245923 -2.83 0.005 -2.515015 -.4586508 _cons | 5.431068 .1464616 37.08 0.000 5.144009 5.718128 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -46.997872 Iteration 1: log likelihood = -29.986288 Iteration 2: log likelihood = -29.603063 Iteration 3: log likelihood = -29.602294 Iteration 4: log likelihood = -29.602294 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 25.94394384 (1/df) Deviance = 12.97197 Pearson = 16.83918596 (1/df) Pearson = 8.419593 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -29.60229422 AIC = 9.88637 BIC = 22.05212354 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .2177836 .4104712 0.53 0.596 -.5867253 1.022292 x2 | -2.045419 .126119 -16.22 0.000 -2.292607 -1.79823 x3 | 2.031236 .3968308 5.12 0.000 1.253462 2.80901 x13 | -2.00526 .4283142 -4.68 0.000 -2.844741 -1.16578 _cons | 3.991329 .3984509 10.02 0.000 3.210379 4.772278 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -56.223113 Iteration 1: log likelihood = -22.941279 Iteration 2: log likelihood = -20.799775 Iteration 3: log likelihood = -20.774415 Iteration 4: log likelihood = -20.77441 Iteration 5: log likelihood = -20.77441 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 8.288175516 (1/df) Deviance = 4.144088 Pearson = 6.260438307 (1/df) Pearson = 3.130219 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.77441006 AIC = 7.364117 BIC = 4.396355218 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.802387 .1222075 -14.75 0.000 -2.041909 -1.562864 x2 | -4.339849 .4086816 -10.62 0.000 -5.14085 -3.538848 x3 | -.1266639 .1621039 -0.78 0.435 -.4443818 .191054 x23 | 2.443434 .4280413 5.71 0.000 1.604488 3.282379 _cons | 6.13312 .1676084 36.59 0.000 5.804613 6.461626 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -58.475335 Iteration 1: log likelihood = -20.905926 Iteration 2: log likelihood = -17.123934 Iteration 3: log likelihood = -17.017128 Iteration 4: log likelihood = -17.016928 Iteration 5: log likelihood = -17.016928 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .7732116279 (1/df) Deviance = .7732116 Pearson = .4156832889 (1/df) Pearson = .4156833 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.01692812 AIC = 6.576265 BIC = -1.172698521 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.684896 .1265693 -13.31 0.000 -1.932968 -1.436825 x2 | -4.122363 .4156054 -9.92 0.000 -4.936935 -3.307792 x3 | -.0266682 .1633138 -0.16 0.870 -.3467575 .293421 x12 | -1.232874 .5287091 -2.33 0.020 -2.269125 -.1966237 x23 | 2.343438 .4285009 5.47 0.000 1.503592 3.183284 _cons | 6.01563 .1708147 35.22 0.000 5.680839 6.35042 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -52.168116 Iteration 1: log likelihood = -22.311411 Iteration 2: log likelihood = -19.436683 Iteration 3: log likelihood = -19.40333 Iteration 4: log likelihood = -19.403257 Iteration 5: log likelihood = -19.403257 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 5.545870145 (1/df) Deviance = 5.54587 Pearson = 4.001367521 (1/df) Pearson = 4.001368 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.40325738 AIC = 7.258074 BIC = 3.599959996 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .5742371 .4166771 1.38 0.168 -.2424351 1.390909 x2 | -1.784269 .1320289 -13.51 0.000 -2.043041 -1.525497 x3 | 2.258782 .3972185 5.69 0.000 1.480249 3.037316 x12 | -1.840072 .523544 -3.51 0.000 -2.866199 -.8139448 x13 | -2.232807 .4286734 -5.21 0.000 -3.072991 -1.392622 _cons | 3.730179 .4003608 9.32 0.000 2.945486 4.514872 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -48.268829 Iteration 1: log likelihood = -24.301767 Iteration 2: log likelihood = -20.672002 Iteration 3: log likelihood = -19.905654 Iteration 4: log likelihood = -19.745698 Iteration 5: log likelihood = -19.709639 Iteration 6: log likelihood = -19.700643 Iteration 7: log likelihood = -19.698903 Iteration 8: log likelihood = -19.698621 Iteration 9: log likelihood = -19.698552 Iteration 10: log likelihood = -19.698538 Iteration 11: log likelihood = -19.698535 Iteration 12: log likelihood = -19.698534 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 6.136423343 (1/df) Deviance = 6.136423 Pearson = 5.037191191 (1/df) Pearson = 5.037191 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.69853397 AIC = 7.342438 BIC = 4.190513194 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -17.39399 2262.115 -0.01 0.994 -4451.057 4416.269 x2 | -19.77884 2262.115 -0.01 0.993 -4453.442 4413.884 x3 | -15.72039 2262.115 -0.01 0.994 -4449.384 4417.943 x13 | 15.60652 2262.115 0.01 0.994 -4418.057 4449.27 x23 | 17.88243 2262.115 0.01 0.994 -4415.781 4451.546 _cons | 21.72472 2262.115 0.01 0.992 -4411.939 4455.388 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 250.6832 .0209272 50.97699 50.286 56.81472 3 6.94e-11 | 2. | 2 228.3931 .021451 40.86189 38.67556 44.75371 2 4.00e-09 | 3. | 3 54.12676 .1587631 22.05212 16.83919 25.94394 2 .0002205 | 4. | 4 460.8718 .0280926 4.396355 6.260438 8.288176 2 .0437082 | 5. | 5 409.7838 .0291777 -1.172698 .4156833 .7732116 1 .5190989 | |-------------------------------------------------------------------------------| 6. | 6 41.68657 .1602888 3.59996 4.001368 5.54587 1 .0454634 | 7. | 7 2.72e+09 5117163 4.190513 5.037191 6.136423 1 .0248087 | +-------------------------------------------------------------------------------+ nk is 627 Buen modelo: Sistema 4to solo < estimacion (6 < 410) BICs = -1.17 3.60 . Estimacion subregistro 410 IC ( 267 ; 553) Estimacion del total 1037 IC ( 894 ; 1180) Estimacion BMA: 344 IC ( -68 ; 756) Total registros = 627 (note: file /tmp/model_info_gov_for_jud_tay.dta not found) (note: file /tmp/bma_info_gov_for_jud_tay.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_tay.dta not found) . . estimacion ong gov for _syd ong gov for cell values: 1 23 33 42 4 181 110 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -65.829287 Iteration 1: log likelihood = -54.94941 Iteration 2: log likelihood = -54.907312 Iteration 3: log likelihood = -54.907308 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 75.07212883 (1/df) Deviance = 25.02404 Pearson = 75.92709821 (1/df) Pearson = 25.30903 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -54.90730807 AIC = 16.83066 BIC = 69.23439838 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.230943 .1547084 -14.42 0.000 -2.534165 -1.92772 x2 | -1.356618 .1498519 -9.05 0.000 -1.650322 -1.062914 x3 | -1.774213 .1487394 -11.93 0.000 -2.065737 -1.48269 _cons | 6.440237 .1611936 39.95 0.000 6.124303 6.75617 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -65.000236 Iteration 1: log likelihood = -54.309151 Iteration 2: log likelihood = -54.245756 Iteration 3: log likelihood = -54.245748 Iteration 4: log likelihood = -54.245748 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 73.74900842 (1/df) Deviance = 36.8745 Pearson = 82.51662823 (1/df) Pearson = 41.25831 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -54.24574786 AIC = 16.92736 BIC = 69.85718813 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.39438 .2126474 -11.26 0.000 -2.811162 -1.977599 x2 | -1.491513 .1931087 -7.72 0.000 -1.869999 -1.113026 x3 | -1.867745 .174301 -10.72 0.000 -2.209369 -1.526122 x12 | .3520783 .3037943 1.16 0.246 -.2433476 .9475042 _cons | 6.568226 .198675 33.06 0.000 6.17883 6.957622 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -45.050291 Iteration 1: log likelihood = -36.334775 Iteration 2: log likelihood = -36.182159 Iteration 3: log likelihood = -36.182126 Iteration 4: log likelihood = -36.182126 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 37.62176404 (1/df) Deviance = 18.81088 Pearson = 40.52969553 (1/df) Pearson = 20.26485 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -36.18212567 AIC = 11.76632 BIC = 33.72994374 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.053197 .2278792 -13.40 0.000 -3.499832 -2.606562 x2 | -1.888151 .2027799 -9.31 0.000 -2.285593 -1.49071 x3 | -2.491386 .2128761 -11.70 0.000 -2.908616 -2.074157 x13 | 1.843359 .3001876 6.14 0.000 1.255003 2.431716 _cons | 7.086648 .2159735 32.81 0.000 6.663348 7.509949 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -39.349389 Iteration 1: log likelihood = -22.50158 Iteration 2: log likelihood = -21.660908 Iteration 3: log likelihood = -21.654412 Iteration 4: log likelihood = -21.654411 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 8.566335651 (1/df) Deviance = 4.283168 Pearson = 8.683288327 (1/df) Pearson = 4.341644 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.65441148 AIC = 7.615546 BIC = 4.674515353 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.643924 .1446848 -11.36 0.000 -1.927501 -1.360347 x2 | -.2401295 .2083613 -1.15 0.249 -.6485102 .1682511 x3 | -.5954049 .2133201 -2.79 0.005 -1.013505 -.1773051 x23 | -3.113277 .5004073 -6.22 0.000 -4.094057 -2.132497 _cons | 5.381594 .2115259 25.44 0.000 4.967011 5.796177 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -38.108922 Iteration 1: log likelihood = -18.212606 Iteration 2: log likelihood = -17.530963 Iteration 3: log likelihood = -17.525822 Iteration 4: log likelihood = -17.525822 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .3091557128 (1/df) Deviance = .3091557 Pearson = .3655468933 (1/df) Pearson = .3655469 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.52582151 AIC = 6.721663 BIC = -1.636754436 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.203973 .1984791 -6.07 0.000 -1.592985 -.814961 x2 | .2544991 .2621567 0.97 0.332 -.2593186 .7683169 x3 | -.2411621 .2326211 -1.04 0.300 -.6970909 .2147668 x12 | -.8383292 .294051 -2.85 0.004 -1.414659 -.2619997 x23 | -3.46752 .5089347 -6.81 0.000 -4.465013 -2.470026 _cons | 4.941642 .251403 19.66 0.000 4.448902 5.434383 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -33.183442 Iteration 1: log likelihood = -25.693883 Iteration 2: log likelihood = -25.455949 Iteration 3: log likelihood = -25.455019 Iteration 4: log likelihood = -25.455018 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 16.1675496 (1/df) Deviance = 16.16755 Pearson = 12.79407014 (1/df) Pearson = 12.79407 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -25.45501845 AIC = 8.987148 BIC = 14.22163946 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.615927 .5321961 -8.67 0.000 -5.659013 -3.572842 x2 | -3.314186 .5090097 -6.51 0.000 -4.311827 -2.316545 x3 | -3.812203 .5054947 -7.54 0.000 -4.802954 -2.821451 x12 | 2.174752 .5604381 3.88 0.000 1.076313 3.27319 x13 | 3.164176 .5480157 5.77 0.000 2.090085 4.238267 _cons | 8.512683 .5144082 16.55 0.000 7.504462 9.520904 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -32.683304 Iteration 1: log likelihood = -18.388254 Iteration 2: log likelihood = -17.399552 Iteration 3: log likelihood = -17.384589 Iteration 4: log likelihood = -17.384575 Iteration 5: log likelihood = -17.384575 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .0266621677 (1/df) Deviance = .0266622 Pearson = .025847424 (1/df) Pearson = .0258474 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.38457474 AIC = 6.681307 BIC = -1.919247981 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.063003 .2213665 -9.32 0.000 -2.496873 -1.629132 x2 | -.6021754 .2593989 -2.32 0.020 -1.110588 -.0937629 x3 | -1.098842 .2860434 -3.84 0.000 -1.659476 -.5382069 x13 | .8531649 .2952741 2.89 0.004 .2744382 1.431892 x23 | -2.751231 .5237182 -5.25 0.000 -3.7777 -1.724762 _cons | 5.800672 .2698382 21.50 0.000 5.271799 6.329546 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 626.555 .0259834 69.2344 75.9271 75.07213 3 2.29e-16 | 2. | 2 712.1053 .0394717 69.85719 82.51662 73.74901 2 1.21e-18 | 3. | 3 1195.893 .0466446 33.72994 40.5297 37.62177 2 1.58e-09 | 4. | 4 217.3684 .0447432 4.674515 8.683289 8.566336 2 .0130151 | 5. | 5 140 .0632035 -1.636754 .3655469 .3091557 1 .5454416 | |-------------------------------------------------------------------------------| 6. | 6 4977.5 .2646158 14.22164 12.79407 16.16755 1 .0003477 | 7. | 7 330.5217 .0728126 -1.919248 .0258474 .0266622 1 .8722735 | +-------------------------------------------------------------------------------+ nk is 394 Modelo aceptable: Sistema 4to solo < estimacion (378 < 509) BICs = -1.92 -1.64 4.67 Estimacion subregistro 331 IC ( 153 ; 509) Estimacion del total 725 IC ( 547 ; 903) Estimacion BMA: 222 IC ( -76 ; 520) Total registros = 394 (note: file /tmp/model_info_ong_gov_for_syd.dta not found) (note: file /tmp/bma_info_ong_gov_for_syd.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_syd.dta not found) . estimacion ong gov jud _syd ong gov jud cell values: 16 8 43 32 101 84 476 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -41.577252 Iteration 1: log likelihood = -22.027332 Iteration 2: log likelihood = -21.815305 Iteration 3: log likelihood = -21.815115 Iteration 4: log likelihood = -21.815115 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 3.430462358 (1/df) Deviance = 1.143487 Pearson = 3.777378012 (1/df) Pearson = 1.259126 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.8151148 AIC = 7.375747 BIC = -2.407268089 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.326331 .1152849 -20.18 0.000 -2.552285 -2.100377 x2 | -1.464263 .0930767 -15.73 0.000 -1.64669 -1.281836 x3 | .2881227 .1167496 2.47 0.014 .0592976 .5169478 _cons | 5.865873 .1239311 47.33 0.000 5.622972 6.108773 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -37.872799 Iteration 1: log likelihood = -21.230171 Iteration 2: log likelihood = -20.763483 Iteration 3: log likelihood = -20.763004 Iteration 4: log likelihood = -20.763004 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.32624008 (1/df) Deviance = .66312 Pearson = 1.299665214 (1/df) Pearson = .6498326 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.76300366 AIC = 7.360858 BIC = -2.565580219 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.42173 .1347703 -17.97 0.000 -2.685875 -2.157585 x2 | -1.518862 .1011688 -15.01 0.000 -1.71715 -1.320575 x3 | .2548922 .1196433 2.13 0.033 .0203957 .4893888 x12 | .3794282 .2554117 1.49 0.137 -.1211695 .8800258 _cons | 5.910526 .1281224 46.13 0.000 5.65941 6.161641 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -43.164914 Iteration 1: log likelihood = -21.789475 Iteration 2: log likelihood = -21.63467 Iteration 3: log likelihood = -21.634607 Iteration 4: log likelihood = -21.634607 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 3.069447675 (1/df) Deviance = 1.534724 Pearson = 3.346980525 (1/df) Pearson = 1.67349 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.63460746 AIC = 7.609888 BIC = -.8223726228 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.429817 .2083878 -11.66 0.000 -2.838249 -2.021384 x2 | -1.483421 .0990701 -14.97 0.000 -1.677595 -1.289247 x3 | .2391461 .1419809 1.68 0.092 -.0391313 .5174236 x13 | .1495119 .2492142 0.60 0.549 -.338939 .6379628 _cons | 5.914238 .1473759 40.13 0.000 5.625386 6.203089 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -40.840328 Iteration 1: log likelihood = -21.830159 Iteration 2: log likelihood = -21.619663 Iteration 3: log likelihood = -21.619577 Iteration 4: log likelihood = -21.619577 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 3.03938681 (1/df) Deviance = 1.519693 Pearson = 3.351942005 (1/df) Pearson = 1.675971 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.61957703 AIC = 7.605593 BIC = -.8524334878 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.289061 .1282117 -17.85 0.000 -2.540351 -2.037771 x2 | -1.329556 .2359477 -5.63 0.000 -1.792005 -.8671068 x3 | .4005596 .2161678 1.85 0.064 -.0231216 .8242407 x23 | -.1601742 .2571869 -0.62 0.533 -.6642513 .3439029 _cons | 5.754797 .2183764 26.35 0.000 5.326787 6.182807 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -37.869406 Iteration 1: log likelihood = -21.208683 Iteration 2: log likelihood = -20.742943 Iteration 3: log likelihood = -20.742458 Iteration 4: log likelihood = -20.742458 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.285149652 (1/df) Deviance = 1.28515 Pearson = 1.256374769 (1/df) Pearson = 1.256375 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.74245845 AIC = 7.640702 BIC = -.660760497 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.404218 .1592377 -15.10 0.000 -2.716318 -2.092118 x2 | -1.470143 .2609539 -5.63 0.000 -1.981604 -.9586833 x3 | .2954642 .2334648 1.27 0.206 -.1621184 .7530468 x12 | .3619157 .2691259 1.34 0.179 -.1655612 .8893927 x23 | -.0550789 .2718867 -0.20 0.839 -.587967 .4778093 _cons | 5.869954 .2379215 24.67 0.000 5.403636 6.336271 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -37.857293 Iteration 1: log likelihood = -20.909525 Iteration 2: log likelihood = -20.434277 Iteration 3: log likelihood = -20.434067 Iteration 4: log likelihood = -20.434067 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .6683662369 (1/df) Deviance = .6683662 Pearson = .6577627119 (1/df) Pearson = .6577627 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.43406674 AIC = 7.55259 BIC = -1.277543912 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.569866 .2283395 -11.25 0.000 -3.017404 -2.122329 x2 | -1.550297 .1095529 -14.15 0.000 -1.765017 -1.335578 x3 | .1843037 .1476677 1.25 0.212 -.1051197 .4737271 x12 | .4108631 .2588471 1.59 0.112 -.096468 .9181941 x13 | .2043543 .2524973 0.81 0.418 -.2905314 .69924 _cons | 5.981114 .1546176 38.68 0.000 5.678069 6.284159 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -39.946477 Iteration 1: log likelihood = -22.088982 Iteration 2: log likelihood = -21.604551 Iteration 3: log likelihood = -21.603103 Iteration 4: log likelihood = -21.603103 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 3.00643786 (1/df) Deviance = 3.006438 Pearson = 3.295854887 (1/df) Pearson = 3.295855 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.60310255 AIC = 7.886601 BIC = 1.060527711 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.351375 .3700064 -6.35 0.000 -3.076575 -1.626176 x2 | -1.386294 .3952847 -3.51 0.000 -2.161038 -.6115506 x3 | .3374364 .4126043 0.82 0.413 -.4712532 1.146126 x13 | .0710704 .3944452 0.18 0.857 -.702028 .8441688 x23 | -.1034356 .4083182 -0.25 0.800 -.9037247 .6968535 _cons | 5.817111 .4100668 14.19 0.000 5.013395 6.620827 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 352.7899 .0153589 -2.407268 3.777378 3.430462 3 .2865284 | 2. | 2 368.9 .0164154 -2.56558 1.299665 1.32624 2 .5221332 | 3. | 3 370.272 .0217196 -.8223726 3.346981 3.069448 2 .1875912 | 4. | 4 315.7015 .0476882 -.8524335 3.351942 3.039387 2 .1871264 | 5. | 5 354.2325 .0566067 -.6607605 1.256375 1.28515 1 .2623384 | |-------------------------------------------------------------------------------| 6. | 6 395.8812 .0239066 -1.277544 .6577627 .6683663 1 .4173509 | 7. | 7 336 .1681548 1.060528 3.295855 3.006438 1 .0694549 | +-------------------------------------------------------------------------------+ nk is 760 Buen modelo: Sistema 4to solo < estimacion (12 < 369) BICs = -2.57 -2.41 -1.28 Estimacion subregistro 369 IC ( 269 ; 469) Estimacion del total 1129 IC ( 1029 ; 1229) Estimacion BMA: 359 IC ( 218 ; 500) Total registros = 760 (note: file /tmp/model_info_ong_gov_jud_syd.dta not found) (note: file /tmp/bma_info_ong_gov_jud_syd.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_syd.dta not found) . estimacion ong for jud _syd ong for jud cell values: 33 1 26 39 102 12 475 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -86.255454 Iteration 1: log likelihood = -59.30309 Iteration 2: log likelihood = -59.179954 Iteration 3: log likelihood = -59.179902 Iteration 4: log likelihood = -59.179902 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 81.60957765 (1/df) Deviance = 27.20319 Pearson = 84.67606922 (1/df) Pearson = 28.22536 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -59.17990215 AIC = 18.0514 BIC = 75.7718472 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.989185 .1124725 -17.69 0.000 -2.209627 -1.768743 x2 | -1.516979 .097887 -15.50 0.000 -1.708834 -1.325124 x3 | 1.225974 .1564427 7.84 0.000 .9193523 1.532597 _cons | 4.902663 .162127 30.24 0.000 4.5849 5.220426 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -69.160486 Iteration 1: log likelihood = -50.422018 Iteration 2: log likelihood = -50.142342 Iteration 3: log likelihood = -50.141785 Iteration 4: log likelihood = -50.141785 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 63.53334428 (1/df) Deviance = 31.76667 Pearson = 65.01734267 (1/df) Pearson = 32.50867 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -50.14178546 AIC = 15.7548 BIC = 59.64152398 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.268815 .1378631 -16.46 0.000 -2.539022 -1.998609 x2 | -1.707004 .1113263 -15.33 0.000 -1.9252 -1.488809 x3 | 1.130161 .1595053 7.09 0.000 .817536 1.442785 x12 | 1.058977 .2391441 4.43 0.000 .5902635 1.527691 _cons | 5.033154 .1659735 30.33 0.000 4.707852 5.358456 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -66.078472 Iteration 1: log likelihood = -43.679191 Iteration 2: log likelihood = -43.444386 Iteration 3: log likelihood = -43.44428 Iteration 4: log likelihood = -43.44428 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 50.13833408 (1/df) Deviance = 25.06917 Pearson = 56.95322386 (1/df) Pearson = 28.47661 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -43.44428037 AIC = 13.84122 BIC = 46.24651378 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.3995654 .3379452 -1.18 0.237 -1.061926 .2627951 x2 | -1.378914 .0959416 -14.37 0.000 -1.566956 -1.190872 x3 | 2.269397 .3015627 7.53 0.000 1.678345 2.86045 x13 | -1.880739 .3645398 -5.16 0.000 -2.595224 -1.166255 _cons | 3.863821 .3042008 12.70 0.000 3.267598 4.460044 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -80.823009 Iteration 1: log likelihood = -39.038109 Iteration 2: log likelihood = -37.547583 Iteration 3: log likelihood = -37.537058 Iteration 4: log likelihood = -37.537055 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 38.32388409 (1/df) Deviance = 19.16194 Pearson = 47.02929375 (1/df) Pearson = 23.51465 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.53705537 AIC = 12.15344 BIC = 34.43206379 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.284082 .1355155 -16.85 0.000 -2.549687 -2.018476 x2 | -3.4797 .3430596 -10.14 0.000 -4.152085 -2.807316 x3 | .1719563 .2067918 0.83 0.406 -.2333483 .5772609 x23 | 2.168369 .3565015 6.08 0.000 1.469639 2.867099 _cons | 5.947643 .2097748 28.35 0.000 5.536492 6.358794 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -47.051851 Iteration 1: log likelihood = -20.678757 Iteration 2: log likelihood = -19.545086 Iteration 3: log likelihood = -19.534073 Iteration 4: log likelihood = -19.534068 Iteration 5: log likelihood = -19.534068 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 2.317910216 (1/df) Deviance = 2.31791 Pearson = 1.880595909 (1/df) Pearson = 1.880596 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.53406843 AIC = 7.295448 BIC = .3720000666 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.905218 .201412 -14.42 0.000 -3.299979 -2.510458 x2 | -4.264844 .3809804 -11.19 0.000 -5.011552 -3.518136 x3 | -.4054651 .2531848 -1.60 0.109 -.9016983 .0907681 x12 | 1.69538 .2806252 6.04 0.000 1.145365 2.245396 x23 | 2.745791 .3852701 7.13 0.000 1.990675 3.500906 _cons | 6.56878 .2573088 25.53 0.000 6.064464 7.073096 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -54.279906 Iteration 1: log likelihood = -37.149446 Iteration 2: log likelihood = -36.90566 Iteration 3: log likelihood = -36.904824 Iteration 4: log likelihood = -36.904824 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 37.05942229 (1/df) Deviance = 37.05942 Pearson = 30.18290877 (1/df) Pearson = 30.18291 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -36.90482447 AIC = 12.25852 BIC = 35.11351214 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.7551018 .3542967 -2.13 0.033 -1.44951 -.0606931 x2 | -1.538342 .1091292 -14.10 0.000 -1.752231 -1.324453 x3 | 2.140066 .305184 7.01 0.000 1.541917 2.738216 x12 | .8903152 .2381293 3.74 0.000 .4235904 1.35704 x13 | -1.751408 .367541 -4.77 0.000 -2.471775 -1.031041 _cons | 4.023249 .3086139 13.04 0.000 3.418377 4.628121 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -66.616644 Iteration 1: log likelihood = -40.292516 Iteration 2: log likelihood = -37.733078 Iteration 3: log likelihood = -37.519769 Iteration 4: log likelihood = -37.516971 Iteration 5: log likelihood = -37.516969 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 38.28371195 (1/df) Deviance = 38.28371 Pearson = 46.84679429 (1/df) Pearson = 46.84679 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -37.5169693 AIC = 12.43342 BIC = 36.3378018 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.484905 1.040832 -2.39 0.017 -4.524898 -.4449113 x2 | -3.66356 1.012738 -3.62 0.000 -5.64849 -1.678629 x3 | -.0292165 1.054101 -0.03 0.978 -2.095217 2.036784 x13 | .2045998 1.049768 0.19 0.845 -1.852908 2.262108 x23 | 2.352228 1.01737 2.31 0.021 .3582191 4.346238 _cons | 6.148466 1.053078 5.84 0.000 4.084472 8.21246 ------------------------------------------------------------------------------ +------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |------------------------------------------------------------------------------| 1. | 1 134.6479 .0262852 75.77185 84.67607 81.60958 3 3.05e-18 | 2. | 2 153.4162 .0275472 59.64153 65.01734 63.53334 2 7.61e-15 | 3. | 3 47.64706 .0925381 46.24651 56.95322 50.13833 2 4.29e-13 | 4. | 4 382.85 .0440055 34.43206 47.02929 38.32388 2 6.13e-11 | 5. | 5 712.5 .0662078 .3720001 1.880596 2.31791 1 .1702664 | |------------------------------------------------------------------------------| 6. | 6 55.88235 .0952425 35.11351 30.18291 37.05942 1 3.93e-08 | 7. | 7 467.9991 1.108972 36.3378 46.84679 38.28371 1 7.68e-12 | +------------------------------------------------------------------------------+ nk is 688 Buen modelo: Sistema 4to solo < estimacion (84 < 712) BICs = 0.37 34.43 35.11 Estimacion subregistro 712 IC ( 349 ; 1075) Estimacion del total 1400 IC ( 1037 ; 1763) Estimacion BMA: 712 IC ( 349 ; 1075) Total registros = 688 (note: file /tmp/model_info_ong_for_jud_syd.dta not found) (note: file /tmp/bma_info_ong_for_jud_syd.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_syd.dta not found) . estimacion gov for jud _syd gov for jud cell values: 5 0 112 92 130 13 389 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -82.080996 Iteration 1: log likelihood = -73.388573 Iteration 2: log likelihood = -73.376505 Iteration 3: log likelihood = -73.376505 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 111.4288416 (1/df) Deviance = 37.14295 Pearson = 98.49261188 (1/df) Pearson = 32.83087 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -73.37650504 AIC = 22.10757 BIC = 105.5911112 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.253124 .087628 -14.30 0.000 -1.424872 -1.081376 x2 | -1.678313 .0965379 -17.39 0.000 -1.867524 -1.489102 x3 | .7356607 .1168737 6.29 0.000 .5065925 .9647289 _cons | 5.297137 .1244587 42.56 0.000 5.053202 5.541071 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -80.786616 Iteration 1: log likelihood = -51.199649 Iteration 2: log likelihood = -48.231975 Iteration 3: log likelihood = -48.192587 Iteration 4: log likelihood = -48.192484 Iteration 5: log likelihood = -48.192484 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 61.06079981 (1/df) Deviance = 30.5304 Pearson = 54.22915963 (1/df) Pearson = 27.11458 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -48.19248413 AIC = 15.19785 BIC = 57.16897951 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.9997022 .0931683 -10.73 0.000 -1.182309 -.8170957 x2 | -1.354978 .1037852 -13.06 0.000 -1.558393 -1.151562 x3 | .855428 .1165006 7.34 0.000 .627091 1.083765 x12 | -2.353705 .4644064 -5.07 0.000 -3.263924 -1.443485 _cons | 5.108151 .1270554 40.20 0.000 4.859127 5.357175 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -64.414457 Iteration 1: log likelihood = -54.158744 Iteration 2: log likelihood = -53.987857 Iteration 3: log likelihood = -53.987314 Iteration 4: log likelihood = -53.987314 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 72.65046006 (1/df) Deviance = 36.32523 Pearson = 51.25366644 (1/df) Pearson = 25.62683 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -53.98731426 AIC = 16.85352 BIC = 68.75863977 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .2718129 .306311 0.89 0.375 -.3285456 .8721715 x2 | -1.47992 .0953611 -15.52 0.000 -1.666824 -1.293015 x3 | 2.001928 .291348 6.87 0.000 1.430897 2.57296 x13 | -1.761543 .3229554 -5.45 0.000 -2.394524 -1.128562 _cons | 4.044869 .2932863 13.79 0.000 3.470038 4.619699 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -63.997042 Iteration 1: log likelihood = -37.545197 Iteration 2: log likelihood = -36.276153 Iteration 3: log likelihood = -36.268386 Iteration 4: log likelihood = -36.268385 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 37.21260216 (1/df) Deviance = 18.6063 Pearson = 27.95189681 (1/df) Pearson = 13.97595 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -36.26838531 AIC = 11.79097 BIC = 33.32078186 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.51447 .1021113 -14.83 0.000 -1.714604 -1.314335 x2 | -3.670098 .3078942 -11.92 0.000 -4.273559 -3.066636 x3 | -.0184413 .1409672 -0.13 0.896 -.2947319 .2578494 x23 | 2.358767 .3228037 7.31 0.000 1.726083 2.99145 _cons | 6.036258 .1459324 41.36 0.000 5.750236 6.32228 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -67.06859 Iteration 1: log likelihood = -22.185874 Iteration 2: log likelihood = -18.230836 Iteration 3: log likelihood = -18.130273 Iteration 4: log likelihood = -18.130102 Iteration 5: log likelihood = -18.130102 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .9360357199 (1/df) Deviance = .9360357 Pearson = .498316498 (1/df) Pearson = .4983165 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.13010209 AIC = 6.894315 BIC = -1.009874429 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.24508 .1072346 -11.61 0.000 -1.455256 -1.034904 x2 | -3.236287 .3154808 -10.26 0.000 -3.854618 -2.617956 x3 | .1967103 .1407059 1.40 0.162 -.0790681 .4724887 x12 | -2.108326 .4674316 -4.51 0.000 -3.024475 -1.192177 x23 | 2.143615 .3226897 6.64 0.000 1.511155 2.776075 _cons | 5.766869 .1495621 38.56 0.000 5.473733 6.060005 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -54.443279 Iteration 1: log likelihood = -23.253669 Iteration 2: log likelihood = -20.645427 Iteration 3: log likelihood = -20.611041 Iteration 4: log likelihood = -20.610987 Iteration 5: log likelihood = -20.610987 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 5.89780626 (1/df) Deviance = 5.897806 Pearson = 4.027987263 (1/df) Pearson = 4.027987 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.61098736 AIC = 7.603139 BIC = 3.951896111 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .8365801 .3133256 2.67 0.008 .2224732 1.450687 x2 | -1.096045 .1013065 -10.82 0.000 -1.294602 -.8974879 x3 | 2.302585 .2908872 7.92 0.000 1.732457 2.872714 x12 | -2.612637 .4638588 -5.63 0.000 -3.521784 -1.703491 x13 | -2.0622 .3225399 -6.39 0.000 -2.694366 -1.430033 _cons | 3.660994 .2952729 12.40 0.000 3.08227 4.239718 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -61.322208 Iteration 1: log likelihood = -38.899095 Iteration 2: log likelihood = -34.678257 Iteration 3: log likelihood = -33.879396 Iteration 4: log likelihood = -33.708228 Iteration 5: log likelihood = -33.666176 Iteration 6: log likelihood = -33.657461 Iteration 7: log likelihood = -33.655607 Iteration 8: log likelihood = -33.655189 Iteration 9: log likelihood = -33.655084 Iteration 10: log likelihood = -33.655064 Iteration 11: log likelihood = -33.65506 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 31.9859524 (1/df) Deviance = 31.98595 Pearson = 24.64411273 (1/df) Pearson = 24.64411 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -33.65506043 AIC = 11.33002 BIC = 30.04004225 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -16.47102 1046.314 -0.02 0.987 -2067.208 2034.266 x2 | -18.42784 1046.314 -0.02 0.986 -2069.165 2032.309 x3 | -14.97951 1046.314 -0.01 0.989 -2065.717 2035.758 x13 | 14.98129 1046.314 0.01 0.989 -2035.756 2065.718 x23 | 17.11652 1046.314 0.02 0.987 -2033.621 2067.854 _cons | 20.99281 1046.314 0.02 0.984 -2029.744 2071.73 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 199.764 .01549 105.5911 98.49261 111.4288 3 3.28e-21 | 2. | 2 165.3644 .0161431 57.16898 54.22916 61.0608 2 1.68e-12 | 3. | 3 57.1037 .0860168 68.75864 51.25367 72.65046 2 7.42e-12 | 4. | 4 418.3248 .0212963 33.32078 27.9519 37.2126 2 8.52e-07 | 5. | 5 319.5357 .0223688 -1.009874 .4983165 .9360357 1 .4802408 | |-------------------------------------------------------------------------------| 6. | 6 38.9 .0871861 3.951896 4.027987 5.897806 1 .0447513 | 7. | 7 1.31e+09 1094772 30.04004 24.64411 31.98595 1 6.90e-07 | +-------------------------------------------------------------------------------+ nk is 741 Buen modelo: Sistema 4to solo < estimacion (31 < 320) BICs = -1.01 3.95 . Estimacion subregistro 320 IC ( 220 ; 420) Estimacion del total 1061 IC ( 961 ; 1161) Estimacion BMA: 272 IC ( -26 ; 570) Total registros = 741 (note: file /tmp/model_info_gov_for_jud_syd.dta not found) (note: file /tmp/bma_info_gov_for_jud_syd.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_syd.dta not found) . . estimacion ong gov for _tayd ong gov for cell values: 0 19 6 9 4 131 59 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -46.202992 Iteration 1: log likelihood = -35.613032 Iteration 2: log likelihood = -35.556212 Iteration 3: log likelihood = -35.556174 Iteration 4: log likelihood = -35.556174 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 42.71191979 (1/df) Deviance = 14.23731 Pearson = 41.06375491 (1/df) Pearson = 13.68792 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -35.55617354 AIC = 11.30176 BIC = 36.87418935 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.825136 .2369994 -11.92 0.000 -3.289646 -2.360625 x2 | -1.079712 .2206948 -4.89 0.000 -1.512266 -.6471577 x3 | -2.054402 .2114738 -9.71 0.000 -2.468883 -1.639921 _cons | 5.938459 .2334556 25.44 0.000 5.480894 6.396023 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -38.162574 Iteration 1: log likelihood = -24.4157 Iteration 2: log likelihood = -24.060529 Iteration 3: log likelihood = -24.060234 Iteration 4: log likelihood = -24.060234 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 19.72004086 (1/df) Deviance = 9.86002 Pearson = 34.61094805 (1/df) Pearson = 17.30547 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -24.06023408 AIC = 8.302924 BIC = 15.82822056 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.196801 .4215432 -9.96 0.000 -5.02301 -3.370591 x2 | -1.999576 .3441503 -5.81 0.000 -2.674098 -1.325054 x3 | -2.766319 .3260204 -8.49 0.000 -3.405307 -2.127331 x12 | 2.235965 .4875835 4.59 0.000 1.280319 3.191611 _cons | 6.843857 .3510534 19.50 0.000 6.155805 7.531908 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -42.760254 Iteration 1: log likelihood = -35.044346 Iteration 2: log likelihood = -34.850394 Iteration 3: log likelihood = -34.850029 Iteration 4: log likelihood = -34.850029 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 41.29963088 (1/df) Deviance = 20.64982 Pearson = 42.52965932 (1/df) Pearson = 21.26483 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -34.85002909 AIC = 11.38572 BIC = 37.40781058 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.98221 .2766168 -10.78 0.000 -3.524369 -2.440051 x2 | -1.168571 .2387295 -4.89 0.000 -1.636472 -.7006697 x3 | -2.171279 .2380663 -9.12 0.000 -2.637881 -1.704678 x13 | .6308343 .508976 1.24 0.215 -.3667403 1.628409 _cons | 6.043768 .2542152 23.77 0.000 5.545516 6.542021 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -26.487962 Iteration 1: log likelihood = -15.48831 Iteration 2: log likelihood = -14.965352 Iteration 3: log likelihood = -14.960535 Iteration 4: log likelihood = -14.960533 Iteration 5: log likelihood = -14.960533 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.520639283 (1/df) Deviance = .7603196 Pearson = 1.054465028 (1/df) Pearson = .5272325 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -14.96053329 AIC = 5.70301 BIC = -2.371181015 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.048982 .2124962 -9.64 0.000 -2.465467 -1.632497 x2 | .6432148 .3914225 1.64 0.100 -.1239592 1.410389 x3 | -.1930332 .4024047 -0.48 0.631 -.9817319 .5956654 x23 | -3.431308 .6469901 -5.30 0.000 -4.699385 -2.16323 _cons | 4.246207 .3953046 10.74 0.000 3.471424 5.02099 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -26.078763 Iteration 1: log likelihood = -15.215054 Iteration 2: log likelihood = -14.738195 Iteration 3: log likelihood = -14.734376 Iteration 4: log likelihood = -14.734375 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.068321815 (1/df) Deviance = 1.068322 Pearson = .5779751427 (1/df) Pearson = .5779751 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -14.73437455 AIC = 5.924107 BIC = -.8775883342 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.285778 .4285042 -5.33 0.000 -3.125631 -1.445925 x2 | .3959549 .549825 0.72 0.471 -.6816822 1.473592 x3 | -.4054651 .5270463 -0.77 0.442 -1.438457 .6275266 x12 | .3249422 .493614 0.66 0.510 -.6425235 1.292408 x23 | -3.218876 .731057 -4.40 0.000 -4.651721 -1.78603 _cons | 4.483003 .5428876 8.26 0.000 3.418962 5.547043 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -26.727037 Iteration 1: log likelihood = -20.099296 Iteration 2: log likelihood = -19.949163 Iteration 3: log likelihood = -19.949013 Iteration 4: log likelihood = -19.949013 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 11.49759946 (1/df) Deviance = 11.4976 Pearson = 9.228571427 (1/df) Pearson = 9.228571 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.94901338 AIC = 7.414004 BIC = 9.551689314 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -5.052546 .5895353 -8.57 0.000 -6.208014 -3.897078 x2 | -2.691243 .5166712 -5.21 0.000 -3.7039 -1.678586 x3 | -3.488903 .5075762 -6.87 0.000 -4.483734 -2.494072 x12 | 2.927632 .6214881 4.71 0.000 1.709538 4.145726 x13 | 1.948458 .6782437 2.87 0.004 .6191247 3.277791 _cons | 7.56644 .5240064 14.44 0.000 6.539407 8.593474 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -27.474811 Iteration 1: log likelihood = -15.333862 Iteration 2: log likelihood = -14.58187 Iteration 3: log likelihood = -14.575613 Iteration 4: log likelihood = -14.575611 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .7507954035 (1/df) Deviance = .7507954 Pearson = .4043954786 (1/df) Pearson = .4043955 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -14.57561135 AIC = 5.878746 BIC = -1.195114746 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.930758 .2454896 -7.86 0.000 -2.411909 -1.449607 x2 | .7472144 .4046513 1.85 0.065 -.0458876 1.540316 x3 | -.0445674 .4337524 -0.10 0.918 -.8947065 .8055717 x13 | -.4206169 .4927523 -0.85 0.393 -1.386394 .5451599 x23 | -3.535307 .655078 -5.40 0.000 -4.819236 -2.251378 _cons | 4.127983 .4139762 9.97 0.000 3.316605 4.939361 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 379.3499 .0545015 36.87419 41.06376 42.71192 3 6.34e-09 | 2. | 2 938.1 .1232385 15.82822 34.61095 19.72004 2 3.05e-08 | 3. | 3 421.4783 .0646254 37.40781 42.52966 41.29963 2 5.82e-10 | 4. | 4 69.84 .1562658 -2.371181 1.054465 1.520639 2 .5902362 | 5. | 5 88.5 .2947269 -.8775883 .5779752 1.068322 1 .4471071 | |-------------------------------------------------------------------------------| 6. | 6 1932.25 .2745827 9.551689 9.228572 11.4976 1 .0023827 | 7. | 7 62.05263 .1713763 -1.195115 .4043955 .7507954 1 .524828 | +-------------------------------------------------------------------------------+ nk is 228 Mal modelo: Sistema 4to solo > estimacion (278 > 127) (note: file /tmp/model_info_ong_gov_for_tayd.dta not found) (note: file /tmp/bma_info_ong_gov_for_tayd.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_tayd.dta not found) . estimacion ong gov jud _tayd ong gov jud cell values: 12 7 9 6 66 69 331 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -55.549784 Iteration 1: log likelihood = -30.594647 Iteration 2: log likelihood = -30.301793 Iteration 3: log likelihood = -30.301314 Iteration 4: log likelihood = -30.301314 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 25.00242118 (1/df) Deviance = 8.33414 Pearson = 31.71759519 (1/df) Pearson = 10.57253 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -30.3013138 AIC = 9.800375 BIC = 19.16469073 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.066299 .1856916 -16.51 0.000 -3.430248 -2.70235 x2 | -1.376046 .1158975 -11.87 0.000 -1.603201 -1.148891 x3 | .1898549 .1475315 1.29 0.198 -.0993016 .4790114 _cons | 5.574884 .156624 35.59 0.000 5.267907 5.881862 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -35.512363 Iteration 1: log likelihood = -19.032347 Iteration 2: log likelihood = -18.727352 Iteration 3: log likelihood = -18.727203 Iteration 4: log likelihood = -18.727203 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.854200313 (1/df) Deviance = .9271002 Pearson = 1.83613435 (1/df) Pearson = .9180672 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.72720336 AIC = 6.779201 BIC = -2.037619985 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.758059 .2743445 -13.70 0.000 -4.295764 -3.220353 x2 | -1.560834 .1265134 -12.34 0.000 -1.808796 -1.312872 x3 | .0591889 .1539135 0.38 0.701 -.2424761 .3608538 x12 | 1.797223 .3678368 4.89 0.000 1.076276 2.51817 _cons | 5.74293 .1634335 35.14 0.000 5.422606 6.063253 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -57.231342 Iteration 1: log likelihood = -30.159393 Iteration 2: log likelihood = -29.88237 Iteration 3: log likelihood = -29.881725 Iteration 4: log likelihood = -29.881725 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 24.16324333 (1/df) Deviance = 12.08162 Pearson = 30.54113434 (1/df) Pearson = 15.27057 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -29.88172487 AIC = 9.966207 BIC = 20.27142304 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.292614 .3175852 -10.37 0.000 -3.91507 -2.670158 x2 | -1.403788 .1210573 -11.60 0.000 -1.641056 -1.166519 x3 | .1263729 .1626535 0.78 0.437 -.1924221 .445168 x13 | .3532001 .388585 0.91 0.363 -.4084124 1.114813 _cons | 5.637894 .1707268 33.02 0.000 5.303276 5.972512 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -49.923658 Iteration 1: log likelihood = -27.231859 Iteration 2: log likelihood = -26.947419 Iteration 3: log likelihood = -26.946901 Iteration 4: log likelihood = -26.946901 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 18.2935949 (1/df) Deviance = 9.146797 Pearson = 21.3602842 (1/df) Pearson = 10.68014 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -26.94690066 AIC = 9.127686 BIC = 14.4017746 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.811981 .194577 -14.45 0.000 -3.193345 -2.430617 x2 | -.3313571 .4620764 -0.72 0.473 -1.23701 .5742959 x3 | 1.166855 .4508856 2.59 0.010 .2831356 2.050575 x23 | -1.14088 .478828 -2.38 0.017 -2.079365 -.202394 _cons | 4.603741 .4522465 10.18 0.000 3.717354 5.490127 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -35.448311 Iteration 1: log likelihood = -18.824115 Iteration 2: log likelihood = -18.486547 Iteration 3: log likelihood = -18.486159 Iteration 4: log likelihood = -18.486159 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.372110733 (1/df) Deviance = 1.372111 Pearson = 1.356710941 (1/df) Pearson = 1.356711 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.48615857 AIC = 6.996045 BIC = -.5737994159 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.604894 .3378347 -10.67 0.000 -4.267038 -2.94275 x2 | -1.197598 .54302 -2.21 0.027 -2.261897 -.1332981 x3 | .4054651 .5270463 0.77 0.442 -.6275266 1.438457 x12 | 1.644058 .4173383 3.94 0.000 .82609 2.462026 x23 | -.3794896 .5511408 -0.69 0.491 -1.459706 .7007265 _cons | 5.396653 .5299046 10.18 0.000 4.358059 6.435247 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -35.116596 Iteration 1: log likelihood = -18.194664 Iteration 2: log likelihood = -17.818201 Iteration 3: log likelihood = -17.817783 Iteration 4: log likelihood = -17.817783 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .0353604586 (1/df) Deviance = .0353605 Pearson = .0353961828 (1/df) Pearson = .0353962 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -17.81778344 AIC = 6.805081 BIC = -1.91054969 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.099931 .3832015 -10.70 0.000 -4.850992 -3.34887 x2 | -1.612464 .134806 -11.96 0.000 -1.876679 -1.348249 x3 | -.0444518 .1721751 -0.26 0.796 -.3819088 .2930053 x12 | 1.848852 .3707707 4.99 0.000 1.122155 2.57555 x13 | .5240248 .3926658 1.33 0.182 -.2455859 1.293636 _cons | 5.84657 .1807358 32.35 0.000 5.492335 6.200806 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -47.123131 Iteration 1: log likelihood = -26.253642 Iteration 2: log likelihood = -26.021531 Iteration 3: log likelihood = -26.021165 Iteration 4: log likelihood = -26.021165 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 16.44212384 (1/df) Deviance = 16.44212 Pearson = 21.57308482 (1/df) Pearson = 21.57308 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -26.02116513 AIC = 9.148904 BIC = 14.49621369 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.288196 .3966735 -5.77 0.000 -3.065662 -1.510731 x2 | .1541507 .5563486 0.28 0.782 -.9362726 1.244574 x3 | 1.697445 .5719128 2.97 0.003 .5765161 2.818373 x13 | -.6512175 .4555083 -1.43 0.153 -1.543997 .2415624 x23 | -1.626387 .5703381 -2.85 0.004 -2.74423 -.5085454 _cons | 4.079956 .5692245 7.17 0.000 2.964296 5.195615 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 263.7191 .0245311 19.16469 31.7176 25.00242 3 6.00e-07 | 2. | 2 311.977 .0267105 -2.03762 1.836134 1.8542 2 .3992901 | 3. | 3 280.8706 .0291476 20.27142 30.54113 24.16324 2 2.33e-07 | 4. | 4 99.85714 .2045269 14.40177 21.36028 18.29359 2 .000023 | 5. | 5 220.6667 .2807989 -.5737994 1.356711 1.372111 1 .2441083 | |-------------------------------------------------------------------------------| 6. | 6 346.0454 .0326654 -1.91055 .0353962 .0353605 1 .8507679 | 7. | 7 59.14286 .3240166 14.49621 21.57308 16.44212 1 3.41e-06 | +-------------------------------------------------------------------------------+ nk is 500 Buen modelo: Sistema 4to solo < estimacion (6 < 312) BICs = -2.04 -1.91 -0.57 Estimacion subregistro 312 IC ( 206 ; 418) Estimacion del total 812 IC ( 706 ; 918) Estimacion BMA: 303 IC ( 107 ; 499) Total registros = 500 (note: file /tmp/model_info_ong_gov_jud_tayd.dta not found) (note: file /tmp/bma_info_ong_gov_jud_tayd.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_tayd.dta not found) . estimacion ong for jud _tayd ong for jud cell values: 5 1 16 12 57 6 340 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -55.788857 Iteration 1: log likelihood = -23.077747 Iteration 2: log likelihood = -22.442033 Iteration 3: log likelihood = -22.441326 Iteration 4: log likelihood = -22.441326 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 13.23594227 (1/df) Deviance = 4.411981 Pearson = 13.88494323 (1/df) Pearson = 4.628314 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.44132554 AIC = 7.554664 BIC = 7.398211819 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.655593 .183592 -14.46 0.000 -3.015427 -2.295759 x2 | -1.872775 .1389569 -13.48 0.000 -2.145125 -1.600424 x3 | 1.430947 .2542446 5.63 0.000 .9326364 1.929257 _cons | 4.393665 .2594087 16.94 0.000 3.885233 4.902096 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -47.944011 Iteration 1: log likelihood = -23.516664 Iteration 2: log likelihood = -22.182257 Iteration 3: log likelihood = -22.164895 Iteration 4: log likelihood = -22.164887 Iteration 5: log likelihood = -22.164887 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 12.68306593 (1/df) Deviance = 6.341533 Pearson = 13.70909646 (1/df) Pearson = 6.854548 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.16488737 AIC = 7.761396 BIC = 8.791245629 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.714743 .2028958 -13.38 0.000 -3.112412 -2.317075 x2 | -1.903813 .1460323 -13.04 0.000 -2.190031 -1.617595 x3 | 1.41227 .2558361 5.52 0.000 .9108404 1.913699 x12 | .3633679 .4729761 0.77 0.442 -.5636483 1.290384 _cons | 4.416676 .2615211 16.89 0.000 3.904104 4.929248 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -44.777346 Iteration 1: log likelihood = -16.968566 Iteration 2: log likelihood = -16.745633 Iteration 3: log likelihood = -16.744837 Iteration 4: log likelihood = -16.744837 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 1.842965447 (1/df) Deviance = .9214827 Pearson = 1.942715645 (1/df) Pearson = .9713578 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.74483713 AIC = 6.212811 BIC = -2.048854851 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.149783 .5070923 -2.27 0.023 -2.143666 -.1559009 x2 | -1.764948 .1363466 -12.94 0.000 -2.032183 -1.497714 x3 | 2.269203 .4274791 5.31 0.000 1.43136 3.107047 x13 | -1.78963 .5543289 -3.23 0.001 -2.876095 -.7031657 _cons | 3.556708 .430415 8.26 0.000 2.71311 4.400306 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -57.646481 Iteration 1: log likelihood = -17.664146 Iteration 2: log likelihood = -16.869893 Iteration 3: log likelihood = -16.862439 Iteration 4: log likelihood = -16.862438 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 2.078166885 (1/df) Deviance = 1.039083 Pearson = 2.574314557 (1/df) Pearson = 1.287157 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.86243785 AIC = 6.246411 BIC = -1.813653413 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.907894 .2189428 -13.28 0.000 -3.337014 -2.478774 x2 | -3.500043 .5189336 -6.74 0.000 -4.517134 -2.482952 x3 | .4289773 .3595051 1.19 0.233 -.2756398 1.133594 x23 | 1.752247 .5368706 3.26 0.001 .6999998 2.804494 _cons | 5.392801 .362311 14.88 0.000 4.682684 6.102917 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -44.398002 Iteration 1: log likelihood = -18.095646 Iteration 2: log likelihood = -15.994238 Iteration 3: log likelihood = -15.956666 Iteration 4: log likelihood = -15.956624 Iteration 5: log likelihood = -15.956624 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .2665399342 (1/df) Deviance = .2665399 Pearson = .3066161949 (1/df) Pearson = .3066162 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -15.95662437 AIC = 6.273321 BIC = -1.679370215 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -3.056357 .2558147 -11.95 0.000 -3.557745 -2.554969 x2 | -3.686325 .5413057 -6.81 0.000 -4.747265 -2.625385 x3 | .2876821 .3818813 0.75 0.451 -.4607915 1.036156 x12 | .7049816 .4979768 1.42 0.157 -.2710349 1.680998 x23 | 1.893542 .5521046 3.43 0.001 .811437 2.975647 _cons | 5.541264 .385713 14.37 0.000 4.78528 6.297247 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -39.168638 Iteration 1: log likelihood = -17.672738 Iteration 2: log likelihood = -16.628559 Iteration 3: log likelihood = -16.615507 Iteration 4: log likelihood = -16.615505 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.584301626 (1/df) Deviance = 1.584302 Pearson = 1.43519945 (1/df) Pearson = 1.435199 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.61550522 AIC = 6.461573 BIC = -.3616085232 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.20686 .5199782 -2.32 0.020 -2.225999 -.187722 x2 | -1.785894 .1431259 -12.48 0.000 -2.066416 -1.505373 x3 | 2.251292 .4291975 5.25 0.000 1.41008 3.092504 x12 | .2454495 .4720868 0.52 0.603 -.6798236 1.170723 x13 | -1.771719 .5556552 -3.19 0.001 -2.860783 -.6826546 _cons | 3.577654 .4326103 8.27 0.000 2.729753 4.425554 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -41.430207 Iteration 1: log likelihood = -17.847718 Iteration 2: log likelihood = -16.512338 Iteration 3: log likelihood = -16.443745 Iteration 4: log likelihood = -16.443288 Iteration 5: log likelihood = -16.443288 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.239866973 (1/df) Deviance = 1.239867 Pearson = 1.410513437 (1/df) Pearson = 1.410513 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.44328789 AIC = 6.412368 BIC = -.7060431765 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.791759 1.080123 -1.66 0.097 -3.908762 .3252436 x2 | -2.484907 1.040833 -2.39 0.017 -4.524902 -.4449115 x3 | 1.54672 1.119346 1.38 0.167 -.6471583 3.740597 x13 | -1.147654 1.103089 -1.04 0.298 -3.309668 1.014359 x23 | .7371102 1.049891 0.70 0.483 -1.320638 2.794859 _cons | 4.276666 1.118034 3.83 0.000 2.08536 6.467972 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 80.93647 .0672929 7.398212 13.88494 13.23594 3 .003066 | 2. | 2 82.82051 .0683933 8.791245 13.7091 12.68307 2 .0010546 | 3. | 3 35.04762 .1852571 -2.048855 1.942716 1.842965 2 .3785686 | 4. | 4 219.8182 .1312693 -1.813653 2.574315 2.078167 2 .2760544 | 5. | 5 255 .1487745 -1.67937 .3066162 .2665399 1 .5797641 | |-------------------------------------------------------------------------------| 6. | 6 35.78947 .1871517 -.3616085 1.435199 1.584302 1 .2309178 | 7. | 7 71.99999 1.25 -.7060432 1.410513 1.239867 1 .2349712 | +-------------------------------------------------------------------------------+ nk is 437 Mal modelo: Sistema 4to solo > estimacion (69 > 66) (note: file /tmp/model_info_ong_for_jud_tayd.dta not found) (note: file /tmp/bma_info_ong_for_jud_tayd.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_tayd.dta not found) . estimacion gov for jud _tayd gov for jud cell values: 4 0 74 76 58 7 282 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -52.475535 Iteration 1: log likelihood = -43.457544 Iteration 2: log likelihood = -43.411569 Iteration 3: log likelihood = -43.411558 Iteration 4: log likelihood = -43.411558 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 54.0532156 (1/df) Deviance = 18.01774 Pearson = 48.39086452 (1/df) Pearson = 16.13029 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -43.41155777 AIC = 13.54616 BIC = 48.21548516 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.258571 .1089526 -11.55 0.000 -1.472115 -1.045028 x2 | -2.207063 .1368474 -16.13 0.000 -2.475279 -1.938847 x3 | .4073575 .1393159 2.92 0.003 .1343033 .6804117 _cons | 5.273716 .1488015 35.44 0.000 4.982071 5.565362 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -55.616074 Iteration 1: log likelihood = -37.412724 Iteration 2: log likelihood = -35.551221 Iteration 3: log likelihood = -35.53998 Iteration 4: log likelihood = -35.539974 Iteration 5: log likelihood = -35.539974 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 38.31004736 (1/df) Deviance = 19.15502 Pearson = 33.15755274 (1/df) Pearson = 16.57878 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -35.53997365 AIC = 11.58285 BIC = 34.41822706 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.107689 .1140153 -9.72 0.000 -1.331155 -.8842227 x2 | -1.943937 .1473684 -13.19 0.000 -2.232773 -1.6551 x3 | .4938143 .139288 3.55 0.000 .2208149 .7668136 x12 | -1.680404 .5276212 -3.18 0.001 -2.714523 -.6462858 _cons | 5.148093 .1514834 33.98 0.000 4.851191 5.444995 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -41.669154 Iteration 1: log likelihood = -32.125389 Iteration 2: log likelihood = -31.90026 Iteration 3: log likelihood = -31.899994 Iteration 4: log likelihood = -31.899994 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 31.03008797 (1/df) Deviance = 15.51504 Pearson = 21.1187112 (1/df) Pearson = 10.55936 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -31.89999396 AIC = 10.54286 BIC = 27.13826767 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .3094221 .4124558 0.75 0.453 -.4989764 1.117821 x2 | -1.941291 .1358081 -14.29 0.000 -2.20747 -1.675112 x3 | 1.807634 .3998788 4.52 0.000 1.023886 2.591382 x13 | -1.781659 .4311397 -4.13 0.000 -2.626677 -.9366405 _cons | 3.887201 .4016229 9.68 0.000 3.100035 4.674368 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -43.866068 Iteration 1: log likelihood = -23.422856 Iteration 2: log likelihood = -22.212 Iteration 3: log likelihood = -22.195684 Iteration 4: log likelihood = -22.195671 Iteration 5: log likelihood = -22.195671 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 11.62144114 (1/df) Deviance = 5.810721 Pearson = 8.841007619 (1/df) Pearson = 4.420504 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -22.19567054 AIC = 7.770192 BIC = 7.729620844 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.492616 .125309 -11.91 0.000 -1.738217 -1.247015 x2 | -4.080204 .4080228 -10.00 0.000 -4.879914 -3.280494 x3 | -.151183 .1625868 -0.93 0.352 -.4698471 .1674812 x23 | 2.332407 .430605 5.42 0.000 1.488437 3.176377 _cons | 5.823349 .1698831 34.28 0.000 5.490385 6.156314 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -46.962822 Iteration 1: log likelihood = -19.036616 Iteration 2: log likelihood = -16.864001 Iteration 3: log likelihood = -16.826601 Iteration 4: log likelihood = -16.826489 Iteration 5: log likelihood = -16.826489 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .8830780258 (1/df) Deviance = .883078 Pearson = .4794044663 (1/df) Pearson = .4794045 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.82648899 AIC = 6.521854 BIC = -1.062832123 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.337842 .1306125 -10.24 0.000 -1.593838 -1.081846 x2 | -3.782384 .4170929 -9.07 0.000 -4.599872 -2.964897 x3 | -.0266682 .1633138 -0.16 0.870 -.3467575 .293421 x12 | -1.450251 .5314548 -2.73 0.006 -2.491883 -.4086186 x23 | 2.207892 .43088 5.12 0.000 1.363383 3.052402 _cons | 5.668575 .1738318 32.61 0.000 5.327871 6.009279 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -42.242429 Iteration 1: log likelihood = -20.883819 Iteration 2: log likelihood = -19.169683 Iteration 3: log likelihood = -19.15789 Iteration 4: log likelihood = -19.157885 Iteration 5: log likelihood = -19.157885 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 5.545870145 (1/df) Deviance = 5.54587 Pearson = 4.001367521 (1/df) Pearson = 4.001368 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.15788505 AIC = 7.187967 BIC = 3.599959996 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .7770418 .4206848 1.85 0.065 -.0474852 1.601569 x2 | -1.581464 .1441786 -10.97 0.000 -1.864049 -1.298879 x3 | 2.114533 .4001231 5.28 0.000 1.330306 2.89876 x12 | -2.042877 .5267392 -3.88 0.000 -3.075267 -1.010487 x13 | -2.088557 .4313664 -4.84 0.000 -2.93402 -1.243095 _cons | 3.527374 .4045301 8.72 0.000 2.73451 4.320239 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -40.933239 Iteration 1: log likelihood = -24.351478 Iteration 2: log likelihood = -21.487232 Iteration 3: log likelihood = -20.919371 Iteration 4: log likelihood = -20.799765 Iteration 5: log likelihood = -20.77119 Iteration 6: log likelihood = -20.764874 Iteration 7: log likelihood = -20.763461 Iteration 8: log likelihood = -20.763234 Iteration 9: log likelihood = -20.763208 Iteration 10: log likelihood = -20.763203 Iteration 11: log likelihood = -20.763202 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 8.756503119 (1/df) Deviance = 8.756503 Pearson = 7.148960489 (1/df) Pearson = 7.14896 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -20.76320153 AIC = 7.646629 BIC = 6.81059297 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -16.99339 1851.452 -0.01 0.993 -3645.773 3611.787 x2 | -19.37818 1851.452 -0.01 0.992 -3648.158 3609.402 x3 | -15.65574 1851.452 -0.01 0.993 -3644.436 3613.124 x13 | 15.52115 1851.452 0.01 0.993 -3613.259 3644.301 x23 | 17.63038 1851.452 0.01 0.992 -3611.15 3646.41 _cons | 21.32413 1851.452 0.01 0.991 -3607.456 3650.104 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 195.1398 .0221419 48.21548 48.39087 54.05322 3 1.76e-10 | 2. | 2 172.1029 .0229472 34.41823 33.15755 38.31005 2 6.31e-08 | 3. | 3 48.77419 .161301 27.13827 21.11871 31.03009 2 .0000259 | 4. | 4 338.1026 .0288603 7.729621 8.841007 11.62144 2 .0120282 | 5. | 5 289.6216 .0302175 -1.062832 .4794045 .883078 1 .4886922 | |-------------------------------------------------------------------------------| 6. | 6 34.03448 .1636446 3.59996 4.001368 5.54587 1 .0454634 | 7. | 7 1.82e+09 3427876 6.810593 7.148961 8.756503 1 .0075007 | +-------------------------------------------------------------------------------+ nk is 501 Buen modelo: Sistema 4to solo < estimacion (5 < 290) BICs = -1.06 3.60 . Estimacion subregistro 290 IC ( 186 ; 394) Estimacion del total 791 IC ( 687 ; 895) Estimacion BMA: 241 IC ( -47 ; 529) Total registros = 501 (note: file /tmp/model_info_gov_for_jud_tayd.dta not found) (note: file /tmp/bma_info_gov_for_jud_tayd.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_tayd.dta not found) . . estimacion ong gov for _sy01_04 ong gov for cell values: 0 22 31 38 3 107 84 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -50.941502 Iteration 1: log likelihood = -46.508135 Iteration 2: log likelihood = -46.482206 Iteration 3: log likelihood = -46.482199 Iteration 4: log likelihood = -46.482199 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 61.49602605 (1/df) Deviance = 20.49868 Pearson = 55.35866679 (1/df) Pearson = 18.45289 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -46.48219852 AIC = 14.42349 BIC = 55.65829561 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.806595 .1648633 -10.96 0.000 -2.129722 -1.483469 x2 | -1.357787 .1621775 -8.37 0.000 -1.675649 -1.039925 x3 | -1.496821 .1620781 -9.24 0.000 -1.814488 -1.179154 _cons | 5.886551 .1768681 33.28 0.000 5.539896 6.233206 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -50.183763 Iteration 1: log likelihood = -45.883452 Iteration 2: log likelihood = -45.861463 Iteration 3: log likelihood = -45.861458 Iteration 4: log likelihood = -45.861458 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 60.25454503 (1/df) Deviance = 30.12727 Pearson = 58.75940959 (1/df) Pearson = 29.3797 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -45.86145801 AIC = 14.53185 BIC = 56.36272474 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.973655 .2254643 -8.75 0.000 -2.415557 -1.531753 x2 | -1.507281 .2131486 -7.07 0.000 -1.925044 -1.089517 x3 | -1.591633 .1881483 -8.46 0.000 -1.960397 -1.222869 x12 | .3642168 .3246223 1.12 0.262 -.2720312 1.000465 _cons | 6.02245 .2174961 27.69 0.000 5.596166 6.448735 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -39.753221 Iteration 1: log likelihood = -35.520446 Iteration 2: log likelihood = -35.483757 Iteration 3: log likelihood = -35.483736 Iteration 4: log likelihood = -35.483736 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 39.49910063 (1/df) Deviance = 19.74955 Pearson = 38.59135211 (1/df) Pearson = 19.29568 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -35.48373581 AIC = 11.56678 BIC = 35.60728033 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.541392 .2457529 -10.34 0.000 -3.023059 -2.059725 x2 | -1.811562 .2157219 -8.40 0.000 -2.234369 -1.388755 x3 | -2.169828 .2349938 -9.23 0.000 -2.630408 -1.709249 x13 | 1.509471 .3227178 4.68 0.000 .8769558 2.141986 _cons | 6.484391 .2363932 27.43 0.000 6.021069 6.947713 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -29.595378 Iteration 1: log likelihood = -18.697183 Iteration 2: log likelihood = -18.221092 Iteration 3: log likelihood = -18.21799 Iteration 4: log likelihood = -18.217989 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 4.967607299 (1/df) Deviance = 2.483804 Pearson = 4.367050929 (1/df) Pearson = 2.183525 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.21798915 AIC = 6.633711 BIC = 1.075787001 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.297566 .1549921 -8.37 0.000 -1.601345 -.9937872 x2 | -.3168702 .2211042 -1.43 0.152 -.7502265 .1164861 x3 | -.4317505 .2232281 -1.93 0.053 -.8692695 .0057686 x23 | -3.329449 .6252327 -5.33 0.000 -4.554883 -2.104016 _cons | 4.935152 .2243621 22.00 0.000 4.495411 5.374894 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -29.147655 Iteration 1: log likelihood = -16.860382 Iteration 2: log likelihood = -16.292825 Iteration 3: log likelihood = -16.288086 Iteration 4: log likelihood = -16.288084 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.107797338 (1/df) Deviance = 1.107797 Pearson = .6139531758 (1/df) Pearson = .6139532 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.28808417 AIC = 6.368024 BIC = -.838112811 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.9968296 .2101495 -4.74 0.000 -1.408715 -.5849441 x2 | .0430751 .2823928 0.15 0.879 -.5104047 .5965549 x3 | -.203599 .2420204 -0.84 0.400 -.6779501 .2707522 x12 | -.6126083 .3141787 -1.95 0.051 -1.228387 .0031707 x23 | -3.557601 .6321859 -5.63 0.000 -4.796662 -2.318539 _cons | 4.634416 .2654781 17.46 0.000 4.114088 5.154743 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -31.017424 Iteration 1: log likelihood = -26.783034 Iteration 2: log likelihood = -26.637172 Iteration 3: log likelihood = -26.636652 Iteration 4: log likelihood = -26.636652 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 21.80493322 (1/df) Deviance = 21.80493 Pearson = 14.99082126 (1/df) Pearson = 14.99082 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -26.63665211 AIC = 9.324758 BIC = 19.85902308 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -4.187442 .6121718 -6.84 0.000 -5.387276 -2.987607 x2 | -3.332204 .5875696 -5.67 0.000 -4.48382 -2.180589 x3 | -3.574217 .585388 -6.11 0.000 -4.721556 -2.426877 x12 | 2.18914 .6365417 3.44 0.001 .9415416 3.436739 x13 | 2.913859 .6257826 4.66 0.000 1.687348 4.140371 _cons | 8.005033 .5954695 13.44 0.000 6.837935 9.172132 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -27.221118 Iteration 1: log likelihood = -17.211949 Iteration 2: log likelihood = -16.664207 Iteration 3: log likelihood = -16.662381 Iteration 4: log likelihood = -16.662381 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.85639141 (1/df) Deviance = 1.856391 Pearson = 1.096851499 (1/df) Pearson = 1.096851 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.6623812 AIC = 6.474966 BIC = -.0895187389 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.581786 .2340947 -6.76 0.000 -2.040604 -1.122969 x2 | -.5465437 .2678999 -2.04 0.041 -1.071618 -.0214696 x3 | -.7792169 .3046826 -2.56 0.011 -1.376384 -.1820501 x13 | .5498655 .313931 1.75 0.080 -.0654279 1.165159 x23 | -3.099776 .6432723 -4.82 0.000 -4.360567 -1.838985 _cons | 5.219373 .2848089 18.33 0.000 4.661157 5.777588 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 360.1608 .0312823 55.65829 55.35867 61.49603 3 5.76e-12 | 2. | 2 412.5882 .0473046 56.36272 58.75941 60.25454 2 1.74e-13 | 3. | 3 654.84 .0558817 35.60728 38.59135 39.4991 2 4.17e-09 | 4. | 4 139.0943 .0503384 1.075787 4.367051 4.967607 2 .1126437 | 5. | 5 102.9677 .0704786 -.8381128 .6139532 1.107797 1 .4333032 | |-------------------------------------------------------------------------------| 6. | 6 2996 .3545839 19.85902 14.99082 21.80493 1 .000108 | 7. | 7 184.8182 .0811161 -.0895187 1.096851 1.856391 1 .2949581 | +-------------------------------------------------------------------------------+ nk is 285 Mal modelo: Sistema 4to solo > estimacion (247 > 160) (note: file /tmp/model_info_ong_gov_for_sy01_04.dta not found) (note: file /tmp/bma_info_ong_gov_for_sy01_04.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_for_sy01_04.dta not found) . estimacion ong gov jud _sy01_04 ong gov jud cell values: 15 7 39 30 64 46 322 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -31.397563 Iteration 1: log likelihood = -21.080873 Iteration 2: log likelihood = -21.042436 Iteration 3: log likelihood = -21.042429 Iteration 4: log likelihood = -21.042429 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 3.687446531 (1/df) Deviance = 1.229149 Pearson = 4.041980598 (1/df) Pearson = 1.347327 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.04242896 AIC = 7.15498 BIC = -2.150283916 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.95733 .1242128 -15.76 0.000 -2.200782 -1.713877 x2 | -1.519639 .1119235 -13.58 0.000 -1.739005 -1.300273 x3 | .3987289 .139303 2.86 0.004 .1257001 .6717578 _cons | 5.358075 .1479354 36.22 0.000 5.068128 5.648023 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -28.76724 Iteration 1: log likelihood = -19.818099 Iteration 2: log likelihood = -19.692555 Iteration 3: log likelihood = -19.692284 Iteration 4: log likelihood = -19.692284 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = .9871572798 (1/df) Deviance = .4935786 Pearson = .9631343623 (1/df) Pearson = .4815672 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.69228434 AIC = 7.054938 BIC = -2.904663018 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.073065 .1452508 -14.27 0.000 -2.357752 -1.788379 x2 | -1.606691 .1252835 -12.82 0.000 -1.852243 -1.36114 x3 | .351844 .1432577 2.46 0.014 .0710641 .632624 x12 | .4636274 .2750332 1.69 0.092 -.0754277 1.002683 _cons | 5.422708 .1537152 35.28 0.000 5.121431 5.723984 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -31.953811 Iteration 1: log likelihood = -21.10375 Iteration 2: log likelihood = -21.034312 Iteration 3: log likelihood = -21.034287 Iteration 4: log likelihood = -21.034287 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 3.67116307 (1/df) Deviance = 1.835582 Pearson = 4.040584241 (1/df) Pearson = 2.020292 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.03428723 AIC = 7.438368 BIC = -.2206572279 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.930893 .241449 -8.00 0.000 -2.404124 -1.457661 x2 | -1.51436 .1191027 -12.71 0.000 -1.747797 -1.280923 x3 | .4140268 .1840143 2.25 0.024 .0533653 .7746882 x13 | -.0359606 .2817922 -0.13 0.898 -.5882631 .5163419 _cons | 5.343002 .1895378 28.19 0.000 4.971514 5.714489 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -31.732421 Iteration 1: log likelihood = -21.132991 Iteration 2: log likelihood = -21.042474 Iteration 3: log likelihood = -21.042421 Iteration 4: log likelihood = -21.042421 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 3.687431487 (1/df) Deviance = 1.843716 Pearson = 4.042134193 (1/df) Pearson = 2.021067 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.04242144 AIC = 7.440692 BIC = -.2043888111 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.957552 .1367781 -14.31 0.000 -2.225632 -1.689472 x2 | -1.520541 .2580045 -5.89 0.000 -2.02622 -1.014861 x3 | .3980453 .2246519 1.77 0.076 -.0422643 .8383548 x23 | .0011107 .286347 0.00 0.997 -.5601192 .5623405 _cons | 5.358749 .2281263 23.49 0.000 4.91163 5.805868 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -28.692774 Iteration 1: log likelihood = -19.717428 Iteration 2: log likelihood = -19.58936 Iteration 3: log likelihood = -19.589079 Iteration 4: log likelihood = -19.589079 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .7807468813 (1/df) Deviance = .7807469 Pearson = .7629328875 (1/df) Pearson = .7629329 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.58907914 AIC = 7.311165 BIC = -1.165163268 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.11099 .1695483 -12.45 0.000 -2.443298 -1.778681 x2 | -1.724217 .2871638 -6.00 0.000 -2.287048 -1.161386 x3 | .2623643 .2428464 1.08 0.280 -.2136059 .7383344 x12 | .501552 .2886037 1.74 0.082 -.0641008 1.067205 x23 | .1367917 .300833 0.45 0.649 -.4528302 .7264136 _cons | 5.512187 .2491585 22.12 0.000 5.023846 6.000529 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -28.769846 Iteration 1: log likelihood = -19.810264 Iteration 2: log likelihood = -19.678773 Iteration 3: log likelihood = -19.678486 Iteration 4: log likelihood = -19.678486 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .9595614569 (1/df) Deviance = .9595615 Pearson = .9399844508 (1/df) Pearson = .9399845 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -19.67848642 AIC = 7.33671 BIC = -.9863486921 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.110145 .2664592 -7.92 0.000 -2.632395 -1.587895 x2 | -1.615668 .1368597 -11.81 0.000 -1.883909 -1.347428 x3 | .3302417 .193298 1.71 0.088 -.0486155 .7090989 x12 | .4726044 .2804958 1.68 0.092 -.0771572 1.022366 x13 | .0478244 .2879404 0.17 0.868 -.5165284 .6121773 _cons | 5.44431 .2011709 27.06 0.000 5.050022 5.838598 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -30.649302 Iteration 1: log likelihood = -21.276586 Iteration 2: log likelihood = -21.023848 Iteration 3: log likelihood = -21.023671 Iteration 4: log likelihood = -21.023671 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 3.649931253 (1/df) Deviance = 3.649931 Pearson = 4.032171747 (1/df) Pearson = 4.032172 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -21.02367132 AIC = 7.721049 BIC = 1.704021104 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.882731 .4057047 -4.64 0.000 -2.677898 -1.087565 x2 | -1.455287 .4197505 -3.47 0.001 -2.277983 -.6325914 x3 | .474012 .4483499 1.06 0.290 -.4047377 1.352762 x13 | -.0841221 .4309356 -0.20 0.845 -.9287403 .7604961 x23 | -.0641429 .4377428 -0.15 0.884 -.9221031 .7938173 _cons | 5.283929 .4448928 11.88 0.000 4.411955 6.155903 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 212.3159 .0218849 -2.150284 4.041981 3.687447 3 .2569665 | 2. | 2 226.4915 .0236284 -2.904663 .9631343 .9871573 2 .6178144 | 3. | 3 209.1395 .0359246 -.2206572 4.040584 3.671163 2 .1326167 | 4. | 4 212.459 .0520416 -.2043888 4.042134 3.687432 2 .132514 | 5. | 5 247.6923 .0620799 -1.165163 .7629329 .7807469 1 .3824122 | |-------------------------------------------------------------------------------| 6. | 6 231.4375 .0404697 -.9863487 .9399844 .9595615 1 .3322818 | 7. | 7 197.1429 .1979296 1.704021 4.032172 3.649931 1 .0446404 | +-------------------------------------------------------------------------------+ nk is 523 Buen modelo: Sistema 4to solo < estimacion (9 < 226) BICs = -2.90 -2.15 -1.17 Estimacion subregistro 226 IC ( 152 ; 300) Estimacion del total 749 IC ( 675 ; 823) Estimacion BMA: 223 IC ( 125 ; 321) Total registros = 523 (note: file /tmp/model_info_ong_gov_jud_sy01_04.dta not found) (note: file /tmp/bma_info_ong_gov_jud_sy01_04.dta not found) (note: file /tmp/tabla_estimacion_ong_gov_jud_sy01_04.dta not found) . estimacion ong for jud _sy01_04 ong for jud cell values: 30 1 24 36 78 9 308 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -64.184223 Iteration 1: log likelihood = -51.699504 Iteration 2: log likelihood = -51.587457 Iteration 3: log likelihood = -51.587374 Iteration 4: log likelihood = -51.587374 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 67.66236411 (1/df) Deviance = 22.55412 Pearson = 67.84315981 (1/df) Pearson = 22.61439 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -51.58737377 AIC = 15.88211 BIC = 61.82463367 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.681667 .1206868 -13.93 0.000 -1.918209 -1.445125 x2 | -1.365054 .1111511 -12.28 0.000 -1.582906 -1.147201 x3 | 1.14456 .1675782 6.83 0.000 .8161132 1.473008 _cons | 4.544147 .1753951 25.91 0.000 4.200378 4.887915 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -55.211621 Iteration 1: log likelihood = -45.788468 Iteration 2: log likelihood = -45.732249 Iteration 3: log likelihood = -45.732227 Iteration 4: log likelihood = -45.732227 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 55.95207075 (1/df) Deviance = 27.97604 Pearson = 55.70608713 (1/df) Pearson = 27.85304 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -45.73222709 AIC = 14.49492 BIC = 52.06025046 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.934737 .1478891 -13.08 0.000 -2.224594 -1.644879 x2 | -1.563173 .1292238 -12.10 0.000 -1.816447 -1.309899 x3 | 1.054161 .1712159 6.16 0.000 .7185835 1.389738 x12 | .9028159 .2561709 3.52 0.000 .4007302 1.404902 _cons | 4.675939 .1804484 25.91 0.000 4.322267 5.029612 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -48.893018 Iteration 1: log likelihood = -38.278617 Iteration 2: log likelihood = -38.178044 Iteration 3: log likelihood = -38.177708 Iteration 4: log likelihood = -38.177708 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 40.8430333 (1/df) Deviance = 20.42152 Pearson = 42.81254294 (1/df) Pearson = 21.40627 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -38.17770836 AIC = 12.33649 BIC = 36.951213 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.0624753 .3810722 -0.16 0.870 -.809363 .6844125 x2 | -1.216735 .1090491 -11.16 0.000 -1.430467 -1.003003 x3 | 2.282444 .3475337 6.57 0.000 1.601291 2.963598 x13 | -1.904378 .4078299 -4.67 0.000 -2.70371 -1.105046 _cons | 3.41396 .3507176 9.73 0.000 2.726566 4.101353 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -55.679512 Iteration 1: log likelihood = -32.352276 Iteration 2: log likelihood = -31.633984 Iteration 3: log likelihood = -31.631825 Iteration 4: log likelihood = -31.631825 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 27.75126598 (1/df) Deviance = 13.87563 Pearson = 32.1208424 (1/df) Pearson = 16.06042 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -31.63182471 AIC = 10.46624 BIC = 23.85944569 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.971553 .1439217 -13.70 0.000 -2.253634 -1.689471 x2 | -3.382848 .3791271 -8.92 0.000 -4.125924 -2.639773 x3 | .1197016 .2162161 0.55 0.580 -.3040742 .5434775 x23 | 2.259844 .3949793 5.72 0.000 1.485699 3.03399 _cons | 5.555072 .2202073 25.23 0.000 5.123473 5.98667 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -34.928899 Iteration 1: log likelihood = -19.264621 Iteration 2: log likelihood = -18.651528 Iteration 3: log likelihood = -18.647576 Iteration 4: log likelihood = -18.647575 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 1.782767387 (1/df) Deviance = 1.782767 Pearson = 1.493412337 (1/df) Pearson = 1.493412 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.64757541 AIC = 7.042164 BIC = -.1631427625 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.552046 .2119279 -12.04 0.000 -2.967417 -2.136675 x2 | -4.137756 .4191788 -9.87 0.000 -4.959332 -3.316181 x3 | -.4054651 .2635231 -1.54 0.124 -.921961 .1110308 x12 | 1.520125 .2977679 5.11 0.000 .9365106 2.103739 x23 | 2.785011 .4227336 6.59 0.000 1.956469 3.613554 _cons | 6.135565 .2696131 22.76 0.000 5.607133 6.663997 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -43.660066 Iteration 1: log likelihood = -34.541001 Iteration 2: log likelihood = -34.437261 Iteration 3: log likelihood = -34.436924 Iteration 4: log likelihood = -34.436924 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 33.3614638 (1/df) Deviance = 33.36146 Pearson = 27.30617069 (1/df) Pearson = 27.30617 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -34.43692362 AIC = 11.55341 BIC = 31.41555365 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.3762126 .3998538 -0.94 0.347 -1.159912 .4074865 x2 | -1.373391 .1267567 -10.83 0.000 -1.62183 -1.124952 x3 | 2.159484 .3520392 6.13 0.000 1.4695 2.849468 x12 | .7130336 .2549353 2.80 0.005 .2133696 1.212698 x13 | -1.781418 .411676 -4.33 0.000 -2.588288 -.9745479 _cons | 3.570616 .3566208 10.01 0.000 2.871652 4.269579 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -48.631868 Iteration 1: log likelihood = -33.252195 Iteration 2: log likelihood = -31.698739 Iteration 3: log likelihood = -31.607515 Iteration 4: log likelihood = -31.606961 Iteration 5: log likelihood = -31.606961 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 27.70153904 (1/df) Deviance = 27.70154 Pearson = 31.96016459 (1/df) Pearson = 31.96016 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -31.60696123 AIC = 10.74485 BIC = 25.75562889 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -2.197225 1.054093 -2.08 0.037 -4.263208 -.1312411 x2 | -3.583519 1.013794 -3.53 0.000 -5.570518 -1.59652 x3 | -.1065459 1.068746 -0.10 0.921 -2.20125 1.988159 x13 | .2303713 1.064058 0.22 0.829 -1.855145 2.315887 x23 | 2.460515 1.019828 2.41 0.016 .4616891 4.459341 _cons | 5.780744 1.067187 5.42 0.000 3.689095 7.872392 ------------------------------------------------------------------------------ +-------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |-------------------------------------------------------------------------------| 1. | 1 94.08009 .0307634 61.82463 67.84316 67.66236 3 1.24e-14 | 2. | 2 107.3333 .0325616 52.06025 55.70609 55.95207 2 8.01e-13 | 3. | 3 30.38532 .1230028 36.95121 42.81254 40.84303 2 5.05e-10 | 4. | 4 258.5454 .0484912 23.85945 32.12084 27.75127 2 1.06e-07 | 5. | 5 462 .0726912 -.1631428 1.493412 1.782767 1 .2216878 | |-------------------------------------------------------------------------------| 6. | 6 35.53846 .1271784 31.41555 27.30617 33.36147 1 1.74e-07 | 7. | 7 324 1.138889 25.75563 31.96017 27.70154 1 1.57e-08 | +-------------------------------------------------------------------------------+ nk is 486 Buen modelo: Sistema 4to solo < estimacion (46 < 462) BICs = -0.16 23.86 25.76 Estimacion subregistro 462 IC ( 215 ; 709) Estimacion del total 948 IC ( 701 ; 1195) Estimacion BMA: 462 IC ( 215 ; 709) Total registros = 486 (note: file /tmp/model_info_ong_for_jud_sy01_04.dta not found) (note: file /tmp/bma_info_ong_for_jud_sy01_04.dta not found) (note: file /tmp/tabla_estimacion_ong_for_jud_sy01_04.dta not found) . estimacion gov for jud _sy01_04 gov for jud cell values: 3 0 76 53 105 10 256 file /tmp/mse_data.dta saved model 1 is x1 x2 x3 Iteration 0: log likelihood = -63.479275 Iteration 1: log likelihood = -57.578879 Iteration 2: log likelihood = -57.564856 Iteration 3: log likelihood = -57.564855 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 3 Scale parameter = 1 Deviance = 82.12149495 (1/df) Deviance = 27.37383 Pearson = 71.64244558 (1/df) Pearson = 23.88082 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -57.56485534 AIC = 17.58996 BIC = 76.2837645 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.290163 .1066432 -12.10 0.000 -1.49918 -1.081147 x2 | -1.431053 .110076 -13.00 0.000 -1.646798 -1.215308 x3 | .9415724 .1465339 6.43 0.000 .6543714 1.228774 _cons | 4.687728 .1554925 30.15 0.000 4.382969 4.992488 ------------------------------------------------------------------------------ model 2 is x1 x2 x3 x12 Iteration 0: log likelihood = -59.648451 Iteration 1: log likelihood = -37.834287 Iteration 2: log likelihood = -35.476234 Iteration 3: log likelihood = -35.427368 Iteration 4: log likelihood = -35.427258 Iteration 5: log likelihood = -35.427258 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 37.84630007 (1/df) Deviance = 18.92315 Pearson = 34.60930838 (1/df) Pearson = 17.30465 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -35.4272579 AIC = 11.55065 BIC = 33.95447977 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -.9798176 .114212 -8.58 0.000 -1.203669 -.7559662 x2 | -1.094698 .1182713 -9.26 0.000 -1.326505 -.8628904 x3 | 1.071801 .1459719 7.34 0.000 .7857013 1.357901 x12 | -2.666502 .5958803 -4.47 0.000 -3.834406 -1.498598 _cons | 4.473376 .1587893 28.17 0.000 4.162155 4.784598 ------------------------------------------------------------------------------ model 3 is x1 x2 x3 x13 Iteration 0: log likelihood = -51.901725 Iteration 1: log likelihood = -45.355991 Iteration 2: log likelihood = -45.277979 Iteration 3: log likelihood = -45.277784 Iteration 4: log likelihood = -45.277784 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 57.5473531 (1/df) Deviance = 28.77368 Pearson = 40.88730117 (1/df) Pearson = 20.44365 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -45.27778442 AIC = 14.36508 BIC = 53.6555328 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .1493289 .3551039 0.42 0.674 -.546662 .8453197 x2 | -1.271112 .1088883 -11.67 0.000 -1.484529 -1.057695 x3 | 2.067915 .3316639 6.23 0.000 1.417866 2.717964 x13 | -1.668759 .3762009 -4.44 0.000 -2.406099 -.9314188 _cons | 3.573697 .3344498 10.69 0.000 2.918188 4.229207 ------------------------------------------------------------------------------ model 4 is x1 x2 x3 x23 Iteration 0: log likelihood = -51.052857 Iteration 1: log likelihood = -33.959936 Iteration 2: log likelihood = -33.25922 Iteration 3: log likelihood = -33.256026 Iteration 4: log likelihood = -33.256026 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 2 Scale parameter = 1 Deviance = 33.50383549 (1/df) Deviance = 16.75192 Pearson = 24.95605609 (1/df) Pearson = 12.47803 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -33.25602561 AIC = 10.93029 BIC = 29.61201519 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.546754 .1239098 -12.48 0.000 -1.789613 -1.303895 x2 | -3.407507 .3595886 -9.48 0.000 -4.112287 -2.702726 x3 | .0950433 .1797665 0.53 0.597 -.2572925 .4473791 x23 | 2.284503 .3762648 6.07 0.000 1.547037 3.021968 _cons | 5.517046 .1849907 29.82 0.000 5.154471 5.879621 ------------------------------------------------------------------------------ model 5 is x1 x2 x3 x12 x23 Iteration 0: log likelihood = -51.462656 Iteration 1: log likelihood = -19.822786 Iteration 2: log likelihood = -16.859471 Iteration 3: log likelihood = -16.773709 Iteration 4: log likelihood = -16.773363 Iteration 5: log likelihood = -16.773363 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = .5385103246 (1/df) Deviance = .5385103 Pearson = .2850241528 (1/df) Pearson = .2850242 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -16.77336303 AIC = 6.506675 BIC = -1.407399824 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -1.214444 .1306298 -9.30 0.000 -1.470474 -.9584144 x2 | -2.907903 .3689894 -7.88 0.000 -3.631109 -2.184698 x3 | .3604414 .1789576 2.01 0.044 .009691 .7111919 x12 | -2.431876 .5992438 -4.06 0.000 -3.606372 -1.257379 x23 | 2.019105 .3758791 5.37 0.000 1.282395 2.755814 _cons | 5.184736 .1895576 27.35 0.000 4.81321 5.556262 ------------------------------------------------------------------------------ model 6 is x1 x2 x3 x12 x13 Iteration 0: log likelihood = -41.393273 Iteration 1: log likelihood = -20.0322 Iteration 2: log likelihood = -18.104851 Iteration 3: log likelihood = -18.067639 Iteration 4: log likelihood = -18.067515 Iteration 5: log likelihood = -18.067515 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 3.126814271 (1/df) Deviance = 3.126814 Pearson = 2.059464231 (1/df) Pearson = 2.059464 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -18.067515 AIC = 6.876433 BIC = 1.180904122 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .7535002 .36397 2.07 0.038 .0401321 1.466868 x2 | -.8912171 .1158881 -7.69 0.000 -1.118354 -.6640805 x3 | 2.351375 .3309438 7.11 0.000 1.702737 3.000013 x12 | -2.869983 .5954119 -4.82 0.000 -4.036969 -1.702997 x13 | -1.952219 .3755662 -5.20 0.000 -2.688316 -1.216123 _cons | 3.193802 .3367938 9.48 0.000 2.533698 3.853906 ------------------------------------------------------------------------------ model 7 is x1 x2 x3 x13 x23 Iteration 0: log likelihood = -49.743801 Iteration 1: log likelihood = -34.860829 Iteration 2: log likelihood = -31.996652 Iteration 3: log likelihood = -31.453706 Iteration 4: log likelihood = -31.337601 Iteration 5: log likelihood = -31.309055 Iteration 6: log likelihood = -31.303145 Iteration 7: log likelihood = -31.301887 Iteration 8: log likelihood = -31.301603 Iteration 9: log likelihood = -31.301532 Iteration 10: log likelihood = -31.301518 Iteration 11: log likelihood = -31.301516 Generalized linear models No. of obs = 7 Optimization : ML: Newton-Raphson Residual df = 1 Scale parameter = 1 Deviance = 29.59481659 (1/df) Deviance = 29.59482 Pearson = 22.38062814 (1/df) Pearson = 22.38063 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] Standard errors : OIM Log likelihood = -31.30151616 AIC = 10.65758 BIC = 27.64890644 ------------------------------------------------------------------------------ cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | -16.59605 1269.951 -0.01 0.990 -2505.654 2472.462 x2 | -18.26376 1269.951 -0.01 0.989 -2507.322 2470.795 x3 | -14.95912 1269.951 -0.01 0.991 -2504.017 2474.099 x13 | 15.07662 1269.951 0.01 0.991 -2473.982 2504.135 x23 | 17.14076 1269.951 0.01 0.989 -2471.918 2506.199 _cons | 20.56634 1269.951 0.02 0.987 -2468.492 2509.625 ------------------------------------------------------------------------------ +------------------------------------------------------------------------------+ | model m000s model_~r bic dev_p dev df pval | |------------------------------------------------------------------------------| 1. | 1 108.6062 .0241779 76.28377 71.64245 82.1215 3 1.90e-15 | 2. | 2 87.65218 .025214 33.95448 34.60931 37.8463 2 3.05e-08 | 3. | 3 35.64815 .1118567 53.65553 40.8873 57.54735 2 1.32e-09 | 4. | 4 248.8987 .0342216 29.61201 24.95606 33.50383 2 3.81e-06 | 5. | 5 178.5263 .0359321 -1.4074 .2850242 .5385103 1 .5934269 | |------------------------------------------------------------------------------| 6. | 6 24.38095 .1134301 1.180904 2.059464 3.126814 1 .1512633 | 7. | 7 8.55e+08 1612776 27.64891 22.38063 29.59482 1 2.24e-06 | +------------------------------------------------------------------------------+ nk is 503 Buen modelo: Sistema 4to solo < estimacion (29 < 179) BICs = -1.41 1.18 . Estimacion subregistro 179 IC ( 108 ; 250) Estimacion del total 682 IC ( 611 ; 753) Estimacion BMA: 116 IC ( -105 ; 337) Total registros = 503 (note: file /tmp/model_info_gov_for_jud_sy01_04.dta not found) (note: file /tmp/bma_info_gov_for_jud_sy01_04.dta not found) (note: file /tmp/tabla_estimacion_gov_for_jud_sy01_04.dta not found) . . log close log: /Users/dguz/Projects/svn/CO/MSE/casanares/desp-denuncias/output/logs/estimaciones.log log type: text closed on: 21 Nov 2007, 12:14:16 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------