------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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 0.0