-------------------------------------------------------------------------------- log: C:\Stata8\nov17.log log type: text opened on: 17 Nov 2004, 10:03:09 ** Es buena idea ampliar la memoria y el matsize de stata desde el comienzo . set mem 2m (2048k) . set matsize 400 . use "C:\Documents and Settings\computob1\Escritorio\Apple.dta", clear ** Esta es una base datos sobre el consumo de manzanas... "ecologicas" y "normales" . desc Contains data from C:\Documents and Settings\computob1\Escritorio\Apple.dta obs: 660 vars: 17 1 Jul 1999 15:05 size: 27,060 (98.7% of memory free) ------------------------------------------------------------------------------ > - storage display value variable name type format label variable label ------------------------------------------------------------------------------ > - id int %8.0g respondent identifier educ byte %8.0g years schooling date str6 %9s date: month/day/year state str2 %9s home state regprc float %9.0g price of regular apples ecoprc float %9.0g price of ecolabeled apples inseason byte %9.0g =1 if interviewed in Nov. hhsize byte %9.0g household size male byte %9.0g =1 if male faminc int %9.0g family income, thousands age byte %9.0g in years reglbs float %9.0g quantity regular apples, pounds ecolbs float %9.0g quantity ecolabeled apples, lbs numlt5 byte %9.0g # in household younger than 5 num5_17 byte %9.0g # in household 5 to 17 num18_64 byte %9.0g # in household 18 to 64 numgt64 byte %9.0g # in household older than 64 ------------------------------------------------------------------------------ Sorted by: . summ Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- id | 660 11729.01 1071.583 10002 13921 educ | 660 14.38182 2.274014 8 20 date | 0 state | 0 regprc | 660 .8827273 .2444687 .59 1.19 -------------+-------------------------------------------------------- ecoprc | 660 1.081515 .295573 .59 1.59 inseason | 660 .3363636 .4728233 0 1 hhsize | 660 2.940909 1.526049 1 9 male | 660 .2621212 .4401218 0 1 faminc | 660 53.40909 35.74122 5 250 -------------+-------------------------------------------------------- age | 660 44.52273 15.21254 19 88 reglbs | 660 1.282323 2.909862 0 42 ecolbs | 660 1.47399 2.525781 0 42 numlt5 | 660 .2863636 .6434888 0 4 num5_17 | 660 .6212121 .994143 0 6 -------------+-------------------------------------------------------- num18_64 | 660 1.804545 1.005136 0 7 numgt64 | 660 .2287879 .5487647 0 3 ** Algunos histogramas: . hist ecoprc (bin=25, start=.58999997, width=.04) . hist age (bin=25, start=19, width=2.76) . hist reglbs (bin=25, start=0, width=1.68) . hist faminc (bin=25, start=5, width=9.8) ** Algunas graficas interesantes . graph twoway scatter ecoprc ecolbs . graph twoway scatter regprc reglbs . graph twoway scatter age faminc ** Una matriz de graficas (para impresionar a tus amigos) . graph matrix age faminc educ reglbs ecolbs ** Diagramas box-plot . graph box ecolbs reglbs . graph box faminc . graph box educ . graph box faminc, over(male) // ingreso familiar, por genero . graph box reglbs, over(male) // consumo de manzanas regulares, por genero **Analizando a detalle las variables dependientes . summ *lbs, detail quantity regular apples, pounds ------------------------------------------------------------- Percentiles Smallest 1% 0 0 5% 0 0 10% 0 0 Obs 660 25% 0 0 Sum of Wgt. 660 50% 0 Mean 1.282323 Largest Std. Dev. 2.909862 75% 2 10.5 90% 3 21 Variance 8.467298 95% 5 42 Skewness 9.188362 99% 10 42 Kurtosis 120.805 quantity ecolabeled apples, lbs ------------------------------------------------------------- Percentiles Smallest 1% 0 0 5% 0 0 10% 0 0 Obs 660 25% 0 0 Sum of Wgt. 660 50% 1 Mean 1.47399 Largest Std. Dev. 2.525781 75% 2 10.5 90% 3 21 Variance 6.37957 95% 5 21 Skewness 8.19729 99% 10 42 Kurtosis 113.2799 ** Noten que mucha gente NO consume manzanas de ningun tipo ** Generando una variable dicotomica (dummy): consumo=1, no consumo manzanas=0 . generate ecobuy = 1 if ecolbs>0 // SI consumen manzanas (248 missing values generated) . replace ecobuy = 0 if ecolbs==0 // NO consumen manzanas (248 real changes made) . inspect ecobuy ecobuy: Number of Observations --------- Non- Total Integers Integers | # Negative - - - | # Zero 248 248 - | # Positive 412 412 - | # # ----- ----- ----- | # # Total 660 660 - | # # Missing - +---------------------- ----- 0 1 660 (2 unique values) . tabulate ecobuy ecobuy | Freq. Percent Cum. ------------+----------------------------------- 0 | 248 37.58 37.58 1 | 412 62.42 100.00 ------------+----------------------------------- Total | 660 100.00 ** Regresiones para una variable dicotomica ** Aplicando OLS: Modelo probabilistico lineal... . reg ecobuy ecoprc regprc educ num* Source | SS df MS Number of obs = 660 -------------+------------------------------ F( 7, 652) = 11.60 Model | 17.1492855 7 2.44989793 Prob > F = 0.0000 Residual | 137.662836 652 .211139319 R-squared = 0.1108 -------------+------------------------------ Adj R-squared = 0.1012 Total | 154.812121 659 .234919759 Root MSE = .4595 ------------------------------------------------------------------------------ ecobuy | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ecoprc | -.8032384 .1096635 -7.32 0.000 -1.018575 -.5879021 regprc | .7139853 .1318495 5.42 0.000 .4550843 .9728862 educ | .0283492 .0080043 3.54 0.000 .012632 .0440665 numlt5 | .0102909 .0282815 0.36 0.716 -.045243 .0658248 num5_17 | .0467916 .0183403 2.55 0.011 .0107784 .0828048 num18_64 | .0139332 .0206875 0.67 0.501 -.026689 .0545553 numgt64 | .0261668 .0384671 0.68 0.497 -.0493676 .1017013 _cons | .3918448 .15159 2.58 0.010 .0941813 .6895083 ------------------------------------------------------------------------------ . predict yhat, xb . summ ecobuy yhat Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- ecobuy | 660 .6242424 .4846852 0 1 yhat | 660 .6242424 .1613171 .2617639 1.020928 ** Noten como el rango maximo de la prediccion YHAT excede a 1 ** Esto no tiene sentido cuando tu variable dependiente es 0 o 1... ** ¿Como sabemos si la prediccion YHAT es correcta? . generate pred = 1 if yhat >0.5 // Si la prediccion es > a 0.5, sí compro (179 missing values generated) . replace pred = 0 if yhat <= 0.50 // Si la prediccion es <= 0.5, no compro (179 real changes made) ** Ahora si, tabulemos las predicciones "redondeadas": . tabulate pred pred | Freq. Percent Cum. ------------+----------------------------------- 0 | 179 27.12 27.12 1 | 481 72.88 100.00 ------------+----------------------------------- Total | 660 100.00 ** Y comparemos con los valores observados: . tabulate ecobuy ecobuy | Freq. Percent Cum. ------------+----------------------------------- 0 | 248 37.58 37.58 1 | 412 62.42 100.00 ------------+----------------------------------- Total | 660 100.00 ** Comparando ambas variables: . tab ecobuy pred | pred ecobuy | 0 1 | Total -----------+----------------------+---------- 0 | 104 144 | 248 1 | 75 337 | 412 -----------+----------------------+---------- Total | 179 481 | 660 ** 75 personas compraron y la prediccion era no comprar, ** 144 no compraron y la prediccion era SI comprar. ** Podemos aplicar un modelo más apropiado a este problema, donde ** el resultado sea la PROBABILIDAD de que COMPRE o no compre manzanas, ** en vez de la prediccion lineal de OLS. ** MODELO PROBIT ** Calculando la probabilidad de comprar manzanas con PROBIT . probit ecobuy ecoprc regprc reglbs educ faminc num5_17 male Iteration 0: log likelihood = -436.88634 Iteration 1: log likelihood = -344.5792 Iteration 2: log likelihood = -308.57392 Iteration 3: log likelihood = -307.20507 Iteration 4: log likelihood = -307.20339 Probit estimates Number of obs = 660 LR chi2(7) = 259.37 Prob > chi2 = 0.0000 Log likelihood = -307.20339 Pseudo R2 = 0.2968 ------------------------------------------------------------------------------ ecobuy | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ecoprc | -2.092458 .3633277 -5.76 0.000 -2.804567 -1.380349 regprc | 1.519567 .4252199 3.57 0.000 .6861519 2.352983 reglbs | -.4532305 .0384078 -11.80 0.000 -.5285085 -.3779526 educ | .0637543 .0271329 2.35 0.019 .0105748 .1169338 faminc | .0022298 .001813 1.23 0.219 -.0013237 .0057832 num5_17 | .2147488 .0633566 3.39 0.001 .0905721 .3389254 male | -.1764743 .1323528 -1.33 0.182 -.435881 .0829323 _cons | .7209499 .4682046 1.54 0.124 -.1967142 1.638614 ------------------------------------------------------------------------------ note: 3 failures and 0 successes completely determined. ** A mayor precio de las manzanas ecologicas, menor prob(compra) ** A mayor precio de manzanas regulares, mayor prob(compra), etc.. ** La pseudo-R2 te indica que proporcion de las predicciones fueron correctas ** Guardando la prediccion de probit . predict yprobit, p // Guarda las probabilidades predichas en YPROBIT . label var yprobit "prediccion de probit" . hist yprobit (bin=25, start=0, width=.03956944) ** Redondeando la prediccion (probabilistica) de probit en una dummy: . generate predprobit = 1 if yprobit >0.5 (186 missing values generated) . replace predprobit = 0 if yprobit <=0.5 (186 real changes made) . tab predprobit predprobit | Freq. Percent Cum. ------------+----------------------------------- 0 | 186 28.18 28.18 1 | 474 71.82 100.00 ------------+----------------------------------- Total | 660 100.00 ** ¿Qué tan buena fue la prediccion de probit? . tab ecobuy predprobit | predprobit ecobuy | 0 1 | Total -----------+----------------------+---------- 0 | 152 96 | 248 1 | 34 378 | 412 -----------+----------------------+---------- Total | 186 474 | 660 . disp 378/412 .91747573 // Predijo 91% de los compradores... . disp 152/248 .61290323 // ...y 61% de los no compradores. . ** Comparemos esto con el exito de las predicciones de OLS anteriores: . tab ecobuy pred | pred ecobuy | 0 1 | Total -----------+----------------------+---------- 0 | 104 144 | 248 1 | 75 337 | 412 -----------+----------------------+---------- Total | 179 481 | 660 ** Las predicciones de OLS eran mas pobres... * ¿Qué pasa si el precio de las manzanas es de $5? . lincom 5*ecoprc ( 1) 5 ecoprc = 0 ------------------------------------------------------------------------------ ecobuy | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | -10.46229 1.816639 -5.76 0.000 -14.02284 -6.901745 ------------------------------------------------------------------------------ * ¿Que pasa si el precio de las manzanas regulares es de $3, y tengo 15 años de EDUC? . lincom 5*ecoprc + 3*regprc + 15*educ ( 1) 5 ecoprc + 3 regprc + 15 educ = 0 ------------------------------------------------------------------------------ ecobuy | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | -4.947274 1.152052 -4.29 0.000 -7.205254 -2.689294 ------------------------------------------------------------------------------ ** Otros modelos PROBIT . probit ecobuy ecoprc regprc educ faminc num5_17 male, nolog ** nolog omite las iteraciones Probit estimates Number of obs = 660 LR chi2(6) = 81.81 Prob > chi2 = 0.0000 Log likelihood = -395.98291 Pseudo R2 = 0.0936 ------------------------------------------------------------------------------ ecobuy | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ecoprc | -2.357935 .3258016 -7.24 0.000 -2.996494 -1.719375 regprc | 2.101101 .3856991 5.45 0.000 1.345144 2.857057 educ | .069247 .0241784 2.86 0.004 .0218582 .1166358 faminc | .0020001 .0015512 1.29 0.197 -.0010402 .0050403 num5_17 | .1199115 .0544466 2.20 0.028 .013198 .2266249 male | -.246504 .1192356 -2.07 0.039 -.4802016 -.0128065 _cons | -.0642605 .4136572 -0.16 0.877 -.8750137 .7464926 ------------------------------------------------------------------------------ ** La pseudo-R2 baja mucho cuando elimino la variable REGLBS (que era muy significativa) ** MODELOS LOGIT ** Otra alternativa para variables dependientes dicotomicas, es el modelo LOGIT ** Probit supone una distribucion NORMAL de la var. dependiente, mientras ** que Logit supone una distribución LOGISTICA. ** La dist. logistica tambien es simetrica y acampanada, pero tiene "colas mas gordas" ** (fatter tails) . logit ecobuy ecoprc regprc reglbs educ faminc num5_17 male, nolog Logit estimates Number of obs = 660 LR chi2(7) = 279.32 Prob > chi2 = 0.0000 Log likelihood = -297.22414 Pseudo R2 = 0.3197 ------------------------------------------------------------------------------ ecobuy | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ecoprc | -3.299204 .6511581 -5.07 0.000 -4.575451 -2.022958 regprc | 2.236951 .7630143 2.93 0.003 .7414702 3.732431 reglbs | -.9934925 .094844 -10.48 0.000 -1.179383 -.8076016 educ | .1168768 .0478768 2.44 0.015 .0230401 .2107136 faminc | .0032825 .0032412 1.01 0.311 -.00307 .0096351 num5_17 | .4043771 .1197733 3.38 0.001 .1696256 .6391285 male | -.297065 .2319983 -1.28 0.200 -.7517733 .1576434 _cons | 1.293257 .8249091 1.57 0.117 -.3235352 2.910049 ------------------------------------------------------------------------------ note: 3 failures and 0 successes completely determined. ** El modelo LOGIT tiene una pseudo-R2 ligeramente mejor que su equivalente PROBIT ** Los coeficientes de LOGIT casi siempre son mayores que los de PROBIT ** pero esto solo se debe a la diferente escala de su funcion de distribucion, y no ** a un mayor "peso especifico" de las variables independientes. . help logit ** Obteniendo las probabilidades predichas de logit: . predict ylogit (option p assumed; Pr(ecobuy)) . summ y* Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- yhat | 660 .6242424 .1613171 .2617639 1.020928 ylogit | 660 .6242424 .2981848 3.63e-18 .9850046 ** Noten como la prediccion de logit (y la de probit tambien) estan acotadas ** entre cero y uno, mientras que la de OLS no lo estaba. ** Histograma de la probabilidad predicha: . hist ylogit (bin=25, start=3.626e-18, width=.03940018) . clear ************************************* . use "C:\Documents and Settings\computob1\Escritorio\MROZ.DTA", clear ** Esta base intenta explicar la participacion de la mujer en el mercado laboral . desc Contains data from C:\Documents and Settings\computob1\Escritorio\MROZ.DTA obs: 753 vars: 22 2 Mar 1999 11:30 size: 39,909 (98.1% of memory free) ------------------------------------------------------------------------------ > - storage display value variable name type format label variable label ------------------------------------------------------------------------------ > - inlf byte %9.0g =1 if in lab frce, 1975 hours int %9.0g hours worked, 1975 kidslt6 byte %9.0g # kids < 6 years kidsge6 byte %9.0g # kids 6-18 age byte %9.0g woman's age in yrs educ byte %9.0g years of schooling wage float %9.0g est. wage from earn, hrs repwage float %9.0g rep. wage at interview in 1976 hushrs int %9.0g hours worked by husband, 1975 husage byte %9.0g husband's age huseduc byte %9.0g husband's years of schooling huswage float %9.0g husband's hourly wage, 1975 faminc float %9.0g family income, 1975 mtr float %9.0g fed. marg. tax rte facing woman motheduc byte %9.0g mother's years of schooling fatheduc byte %9.0g father's years of schooling unem float %9.0g unem. rate in county of resid. city byte %9.0g =1 if live in SMSA exper byte %9.0g actual labor mkt exper nwifeinc float %9.0g (faminc - wage*hours)/1000 lwage float %9.0g log(wage) expersq int %9.0g exper^2 ------------------------------------------------------------------------------ > - Sorted by: . summ Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- inlf | 753 .5683931 .4956295 0 1 hours | 753 740.5764 871.3142 0 4950 kidslt6 | 753 .2377158 .523959 0 3 kidsge6 | 753 1.353254 1.319874 0 8 age | 753 42.53785 8.072574 30 60 -------------+-------------------------------------------------------- educ | 753 12.28685 2.280246 5 17 wage | 428 4.177682 3.310282 .1282 25 repwage | 753 1.849734 2.419887 0 9.98 hushrs | 753 2267.271 595.5666 175 5010 husage | 753 45.12085 8.058793 30 60 -------------+-------------------------------------------------------- huseduc | 753 12.49137 3.020804 3 17 huswage | 753 7.482179 4.230559 .4121 40.509 faminc | 753 23080.59 12190.2 1500 96000 mtr | 753 .6788632 .0834955 .4415 .9415 motheduc | 753 9.250996 3.367468 0 17 -------------+-------------------------------------------------------- fatheduc | 753 8.808765 3.57229 0 17 unem | 753 8.623506 3.114934 3 14 city | 753 .6427623 .4795042 0 1 exper | 753 10.63081 8.06913 0 45 nwifeinc | 753 20.12896 11.6348 -.0290575 96 -------------+-------------------------------------------------------- lwage | 428 1.190173 .7231978 -2.054164 3.218876 expersq | 753 178.0385 249.6308 0 2025 ** Matriz de correlaciones . corr kids* educ hus* *educ (obs=753) | kidslt6 kidsge6 educ hushrs husage huseduc huswage -------------+--------------------------------------------------------------- kidslt6 | 1.0000 kidsge6 | 0.0842 1.0000 educ | 0.1087 -0.0589 1.0000 hushrs | 0.0243 0.0994 0.0789 1.0000 husage | -0.4430 -0.3502 -0.1335 -0.0954 1.0000 huseduc | 0.1336 0.0094 0.6120 0.1078 -0.1953 1.0000 huswage | 0.0324 -0.0297 0.2849 -0.2360 0.0197 0.3947 1.0000 motheduc | 0.1078 0.0324 0.4353 0.0534 -0.2275 0.3245 0.1267 fatheduc | 0.0961 -0.0268 0.4425 0.0503 -0.1350 0.3667 0.1932 | educ huseduc motheduc fatheduc -------------+------------------------------------ educ | 1.0000 huseduc | 0.6120 1.0000 motheduc | 0.4353 0.3245 1.0000 fatheduc | 0.4425 0.3667 0.5731 1.0000 ** Un Modelo OLS (erroneo) para la variable binaria INLF (in labor force): . . reg inlf kids* educ hus* *educ Source | SS df MS Number of obs = 753 -------------+------------------------------ F( 9, 743) = 14.59 Model | 27.7399587 9 3.08221763 Prob > F = 0.0000 Residual | 156.987797 743 .211289094 R-squared = 0.1502 -------------+------------------------------ Adj R-squared = 0.1399 Total | 184.727756 752 .245648611 Root MSE = .45966 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- kidslt6 | -.3000199 .0358927 -8.36 0.000 -.3704831 -.2295567 kidsge6 | -.0084823 .0137492 -0.62 0.537 -.0354741 .0185096 educ | .0600492 .0099935 6.01 0.000 .0404302 .0796681 hushrs | -.0001039 .0000299 -3.48 0.001 -.0001626 -.0000452 husage | -.0124777 .0025607 -4.87 0.000 -.0175047 -.0074507 huseduc | -.0077128 .0075752 -1.02 0.309 -.0225842 .0071586 huswage | -.0168186 .0045686 -3.68 0.000 -.0257875 -.0078498 educ | (dropped) huseduc | (dropped) motheduc | .0011592 .0063672 0.18 0.856 -.0113407 .013659 fatheduc | -.0021217 .0059865 -0.35 0.723 -.0138741 .0096307 _cons | .9421025 .1805773 5.22 0.000 .5876 1.296605 ------------------------------------------------------------------------------ ** Noten como STATA "tiro" dos variables por que las introduje dos veces por error ** Un MODELO PROBIT para INLF . probit inlf kids* hus* *educ, nolog Probit estimates Number of obs = 753 LR chi2(9) = 123.93 Prob > chi2 = 0.0000 Log likelihood = -452.90987 Pseudo R2 = 0.1203 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- kidslt6 | -.9043187 .1152472 -7.85 0.000 -1.130199 -.6784383 kidsge6 | -.0272939 .0395706 -0.69 0.490 -.104851 .0502631 educ | .1752229 .0298124 5.88 0.000 .1167916 .2336542 hushrs | -.0003071 .0000869 -3.54 0.000 -.0004773 -.0001368 husage | -.0371826 .0076722 -4.85 0.000 -.0522197 -.0221454 huseduc | -.0226122 .0218456 -1.04 0.301 -.0654287 .0202044 huswage | -.0516179 .0140862 -3.66 0.000 -.0792264 -.0240094 motheduc | .0027484 .0183272 0.15 0.881 -.0331724 .0386691 fatheduc | -.0045866 .0173661 -0.26 0.792 -.0386236 .0294503 _cons | 1.344228 .5340123 2.52 0.012 .2975836 2.390873 ------------------------------------------------------------------------------ ** Obteniendo las probabilidades predichas: . predict yprobit (option p assumed; Pr(inlf)) . hist yprobit (bin=27, start=.00139169, width=.0356881) ** MODELO LOGIT para INLF . logit inlf kids* hus* *educ, nolog Logit estimates Number of obs = 753 LR chi2(9) = 124.09 Prob > chi2 = 0.0000 Log likelihood = -452.82882 Pseudo R2 = 0.1205 ------------------------------------------------------------------------------ inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- kidslt6 | -1.496063 .1974564 -7.58 0.000 -1.88307 -1.109055 kidsge6 | -.0462703 .0660042 -0.70 0.483 -.1756362 .0830956 educ | .2890505 .0504301 5.73 0.000 .1902093 .3878918 hushrs | -.000515 .0001467 -3.51 0.000 -.0008025 -.0002274 husage | -.0620702 .012936 -4.80 0.000 -.0874244 -.036716 huseduc | -.0357898 .0362697 -0.99 0.324 -.106877 .0352975 huswage | -.0851693 .0234725 -3.63 0.000 -.1311745 -.0391641 motheduc | .0062894 .0303268 0.21 0.836 -.0531499 .0657288 fatheduc | -.0089954 .0288539 -0.31 0.755 -.065548 .0475573 _cons | 2.246155 .8927149 2.52 0.012 .4964656 3.995844 ------------------------------------------------------------------------------ ** El impacto marginal de coeficientes de LOGIT/PROBIT no es constante a lo largo de ** la distribucion de probabilidad (esto lo veran el proximo semestre) ** Pero podemos evaluar el impacto marginal de las variables indep en, digamos, ** los valores medios de nuestra muestra: . dprobit inlf kids* hus* *educ, nolog Probit estimates Number of obs = 753 LR chi2(9) = 123.93 Prob > chi2 = 0.0000 Log likelihood = -452.90987 Pseudo R2 = 0.1203 ------------------------------------------------------------------------------ inlf | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- kidslt6 | -.3544754 .0452918 -7.85 0.000 .237716 -.443246 -.265705 kidsge6 | -.0106987 .0155093 -0.69 0.490 1.35325 -.041096 .019699 educ | .068684 .0116761 5.88 0.000 12.2869 .045799 .091569 hushrs | -.0001204 .0000341 -3.54 0.000 2267.27 -.000187 -.000054 husage | -.0145749 .0030039 -4.85 0.000 45.1208 -.020462 -.008687 huseduc | -.0088635 .0085614 -1.04 0.301 12.4914 -.025644 .007916 huswage | -.0202332 .0055256 -3.66 0.000 7.48218 -.031063 -.009403 motheduc | .0010773 .0071839 0.15 0.881 9.251 -.013003 .015157 fatheduc | -.0017979 .0068072 -0.26 0.792 8.80876 -.01514 .011544 ---------+-------------------------------------------------------------------- obs. P | .5683931 pred. P | .5744202 (at x-bar) ------------------------------------------------------------------------------ z and P>|z| are the test of the underlying coefficient being 0 ** DPROBIT te indica el impacto marginal de cada variable en la prob(Y=1), estimado ** en los valores medios de cada variable--pero esto lo puedes ajustar a tu gusto */ NOTA IMPORTANTE: El que hayamos estimado modelos PROBIT y LOGIT no significa ni supone que ya saben todo lo necesario para utilizarlos. PROBIT y LOGIT pertenecen a una familia de métodos llamados de "Métodos de Maxima Verosimilitud" y que verán con más cuidado el próximo semestre. La idea de verlos rapidamente en este curso es que no "les tengan miedo", y de paso ayudarlos con sus estudios de replicacion y a entender la literatura que utiliza estos metodos. . clear . log close log: C:\Stata8\nov17.log log type: text closed on: 17 Nov 2004, 11:00:16 ------------------------------------------------------------------------------