Stata program for Probit/Logit Models
STATA Results for Probit/Logit Models
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STATA Results for Probit/Logit Models
workplace.log ------------------------------------------------------------------------------ log: c:\bill\jpsm\workplace1.log log type: text opened on: 4 Nov 2004, 07:29:21 . * use the workplace data set; . use c:\bill\jpsm\workplace1; . * print out variable labels; . desc; Contains data from c:\bill\jpsm\workplace1.dta obs: 16,258 vars: 10 28 Oct 2004 05:27 size: 325,160 (96.9% of memory free) ------------------------------------------------------------------------------ > - storage display value variable name type format label variable label ------------------------------------------------------------------------------ > - smoker byte %9.0g is current smoking worka byte %9.0g has workplace smoking bans age byte %9.0g age in years male byte %9.0g male black byte %9.0g black hispanic byte %9.0g hispanic incomel float %9.0g log income hsgrad byte %9.0g is hs graduate somecol byte %9.0g has some college college float %9.0g ------------------------------------------------------------------------------ > - Sorted by: . * get summary statistics; . sum; Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- smoker | 16258 .25163 .433963 0 1 worka | 16258 .6851396 .4644745 0 1 age | 16258 38.54742 11.96189 18 87 male | 16258 .3947595 .488814 0 1 black | 16258 .1119449 .3153083 0 1 -------------+-------------------------------------------------------- hispanic | 16258 .0607086 .2388023 0 1 incomel | 16258 10.42097 .7624525 6.214608 11.22524 hsgrad | 16258 .3355271 .4721889 0 1 somecol | 16258 .2685447 .4432161 0 1 college | 16258 .3293763 .4700012 0 1 . * run a linear probability model for comparison purposes; . * estimate white standard errors to control for heteroskedasticity; . reg smoker age incomel male black hispanic > hsgrad somecol college worka, robust; Regression with robust standard errors Number of obs = 16258 F( 9, 16248) = 99.26 Prob > F = 0.0000 R-squared = 0.0488 Root MSE = .42336 ------------------------------------------------------------------------------ | Robust smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0004776 .0002806 -1.70 0.089 -.0010276 .0000725 incomel | -.0287361 .0047823 -6.01 0.000 -.03811 -.0193621 male | .0168615 .0069542 2.42 0.015 .0032305 .0304926 black | -.0356723 .0110203 -3.24 0.001 -.0572732 -.0140714 hispanic | -.070582 .0136691 -5.16 0.000 -.097375 -.043789 hsgrad | -.0661429 .0162279 -4.08 0.000 -.0979514 -.0343345 somecol | -.1312175 .0164726 -7.97 0.000 -.1635056 -.0989293 college | -.2406109 .0162568 -14.80 0.000 -.272476 -.2087459 worka | -.066076 .0074879 -8.82 0.000 -.080753 -.051399 _cons | .7530714 .0494255 15.24 0.000 .6561919 .8499509 ------------------------------------------------------------------------------ . * run probit model; . probit smoker age incomel male black hispanic > hsgrad somecol college worka; Iteration 0: log likelihood = -9171.443 Iteration 1: log likelihood = -8764.068 Iteration 2: log likelihood = -8761.7211 Iteration 3: log likelihood = -8761.7208 Probit estimates Number of obs = 16258 LR chi2(9) = 819.44 Prob > chi2 = 0.0000 Log likelihood = -8761.7208 Pseudo R2 = 0.0447 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0012684 .0009316 -1.36 0.173 -.0030943 .0005574 incomel | -.092812 .0151496 -6.13 0.000 -.1225047 -.0631193 male | .0533213 .0229297 2.33 0.020 .0083799 .0982627 black | -.1060518 .034918 -3.04 0.002 -.17449 -.0376137 hispanic | -.2281468 .0475128 -4.80 0.000 -.3212701 -.1350235 hsgrad | -.1748765 .0436392 -4.01 0.000 -.2604078 -.0893453 somecol | -.363869 .0451757 -8.05 0.000 -.4524118 -.2753262 college | -.7689528 .0466418 -16.49 0.000 -.860369 -.6775366 worka | -.2093287 .0231425 -9.05 0.000 -.2546873 -.1639702 _cons | .870543 .154056 5.65 0.000 .5685989 1.172487 ------------------------------------------------------------------------------ . *predict probability of smoking; . predict pred_prob_smoke; (option p assumed; Pr(smoker)) . * get detailed descriptive data about predicted prob; . sum pred_prob, detail; Pr(smoker) ------------------------------------------------------------- Percentiles Smallest 1% .0959301 .0615221 5% .1155022 .0622963 10% .1237434 .0633929 Obs 16258 25% .1620851 .0733495 Sum of Wgt. 16258 50% .2569962 Mean .2516653 Largest Std. Dev. .0960007 75% .3187975 .5619798 90% .3795704 .5655878 Variance .0092161 95% .4039573 .5684112 Skewness .1520254 99% .4672697 .6203823 Kurtosis 2.149247 . * predict binary outcome with 50% cutoff; . gen pred_smoke1=pred_prob_smoke>=.5; . label variable pred_smoke1 "predicted smoking, 50% cutoff"; . tab smoker pred_smoke1, row col cell; +-------------------+ | Key | |-------------------| | frequency | | row percentage | | column percentage | | cell percentage | +-------------------+ | predicted smoking, is current | 50% cutoff smoking | 0 1 | Total -----------+----------------------+---------- 0 | 12,153 14 | 12,167 | 99.88 0.12 | 100.00 | 74.93 35.90 | 74.84 | 74.75 0.09 | 74.84 -----------+----------------------+---------- 1 | 4,066 25 | 4,091 | 99.39 0.61 | 100.00 | 25.07 64.10 | 25.16 | 25.01 0.15 | 25.16 -----------+----------------------+---------- Total | 16,219 39 | 16,258 | 99.76 0.24 | 100.00 | 100.00 100.00 | 100.00 | 99.76 0.24 | 100.00 . * ask for marginal effects/treatment effects; . mfx compute; Marginal effects after probit y = Pr(smoker) (predict) = .24093439 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- age | -.0003951 .00029 -1.36 0.173 -.000964 .000174 38.5474 incomel | -.0289139 .00472 -6.13 0.000 -.03816 -.019668 10.421 male*| .0166757 .0072 2.32 0.021 .002568 .030783 .39476 black*| -.0320621 .01023 -3.13 0.002 -.052111 -.012013 .111945 hispanic*| -.0658551 .01259 -5.23 0.000 -.090536 -.041174 .060709 hsgrad*| -.053335 .01302 -4.10 0.000 -.07885 -.02782 .335527 somecol*| -.1062358 .01228 -8.65 0.000 -.130308 -.082164 .268545 college*| -.2149199 .01146 -18.76 0.000 -.237378 -.192462 .329376 worka*| -.0668959 .00756 -8.84 0.000 -.08172 -.052072 .68514 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . * the same type of variables can be produced with; . * prchange. this command is however more flexible; . * in that you can change the reference individual; . prchange, help; probit: Changes in Predicted Probabilities for smoker min->max 0->1 -+1/2 -+sd/2 MargEfct age -0.0269 -0.0004 -0.0004 -0.0047 -0.0004 incomel -0.1589 -0.0361 -0.0289 -0.0220 -0.0289 male 0.0167 0.0167 0.0166 0.0081 0.0166 black -0.0321 -0.0321 -0.0330 -0.0104 -0.0330 hispanic -0.0659 -0.0659 -0.0710 -0.0170 -0.0711 hsgrad -0.0533 -0.0533 -0.0544 -0.0257 -0.0545 somecol -0.1062 -0.1062 -0.1130 -0.0502 -0.1134 college -0.2149 -0.2149 -0.2366 -0.1123 -0.2396 worka -0.0669 -0.0669 -0.0652 -0.0303 -0.0652 0 1
age incomel male black hispanic hsgrad somecol x= 38.5474 10.421 .39476 .111945 .060709 .335527 .268545 sd(x)= 11.9619 .762452 .488814 .315308 .238802 .472189 .443216 college worka x= .329376 .68514 sd(x)= .470001 .464475 Pr(y|x): probability of observing each y for specified x values Avg|Chg|: average of absolute value of the change across categories Min->Max: change in predicted probability as x changes from its minimum to its maximum 0->1: change in predicted probability as x changes from 0 to 1 -+1/2: change in predicted probability as x changes from 1/2 unit below base value to 1/2 unit above -+sd/2: change in predicted probability as x changes from 1/2 standard dev below base to 1/2 standard dev above MargEfct: the partial derivative of the predicted probability/rate with respect to a given independent variable . * get marginal effect/treatment effects for specific person; . * male, age 40, college educ, white, without workplace smoking ban; . * if a variable is not specified, its value is assumed to be; . * the sample mean. in this case, the only variable i am not; . * listing is mean log income; . prchange, x(age=40 black=0 hispanic=0 hsgrad=0 somecol=0 worka=0); probit: Changes in Predicted Probabilities for smoker min->max 0->1 -+1/2 -+sd/2 MargEfct age -0.0323 -0.0005 -0.0005 -0.0056 -0.0005 incomel -0.1795 -0.0320 -0.0344 -0.0263 -0.0345 male 0.0198 0.0198 0.0198 0.0097 0.0198 black -0.0385 -0.0385 -0.0394 -0.0124 -0.0394 hispanic -0.0804 -0.0804 -0.0845 -0.0202 -0.0847 hsgrad -0.0625 -0.0625 -0.0648 -0.0306 -0.0649 somecol -0.1235 -0.1235 -0.1344 -0.0598 -0.1351 college -0.2644 -0.2644 -0.2795 -0.1335 -0.2854 worka -0.0742 -0.0742 -0.0776 -0.0361 -0.0777 0 1
age incomel male black hispanic hsgrad somecol x= 40 10.421 .39476 0 0 0 0 sd(x)= 11.9619 .762452 .488814 .315308 .238802 .472189 .443216 college worka x= .329376 0 sd(x)= .470001 .464475 . * using a wald test, test the null hypothesis that; . * all the education coefficients are zero; . test hsgrad somecol college; ( 1) hsgrad = 0 ( 2) somecol = 0 ( 3) college = 0 chi2( 3) = 504.78 Prob > chi2 = 0.0000 . * how to run the same tets with a -2 log like test; . * estimate the unresticted model and save the estimates ; . * in urmodel; . probit smoker age incomel male black hispanic > hsgrad somecol college worka; Iteration 0: log likelihood = -9171.443 Iteration 1: log likelihood = -8764.068 Iteration 2: log likelihood = -8761.7211 Iteration 3: log likelihood = -8761.7208 Probit estimates Number of obs = 16258 LR chi2(9) = 819.44 Prob > chi2 = 0.0000 Log likelihood = -8761.7208 Pseudo R2 = 0.0447 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0012684 .0009316 -1.36 0.173 -.0030943 .0005574 incomel | -.092812 .0151496 -6.13 0.000 -.1225047 -.0631193 male | .0533213 .0229297 2.33 0.020 .0083799 .0982627 black | -.1060518 .034918 -3.04 0.002 -.17449 -.0376137 hispanic | -.2281468 .0475128 -4.80 0.000 -.3212701 -.1350235 hsgrad | -.1748765 .0436392 -4.01 0.000 -.2604078 -.0893453 somecol | -.363869 .0451757 -8.05 0.000 -.4524118 -.2753262 college | -.7689528 .0466418 -16.49 0.000 -.860369 -.6775366 worka | -.2093287 .0231425 -9.05 0.000 -.2546873 -.1639702 _cons | .870543 .154056 5.65 0.000 .5685989 1.172487 ------------------------------------------------------------------------------ . estimates store urmodel; . * estimate the restricted model. save results in rmodel; . probit smoker age incomel male black hispanic > worka; Iteration 0: log likelihood = -9171.443 Iteration 1: log likelihood = -9022.2473 Iteration 2: log likelihood = -9022.1031 Probit estimates Number of obs = 16258 LR chi2(6) = 298.68 Prob > chi2 = 0.0000 Log likelihood = -9022.1031 Pseudo R2 = 0.0163 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0003514 .0009163 0.38 0.701 -.0014445 .0021473 incomel | -.1802868 .0143242 -12.59 0.000 -.2083617 -.152212 male | -.0117546 .0223519 -0.53 0.599 -.0555635 .0320543 black | -.0650982 .0345516 -1.88 0.060 -.1328181 .0026217 hispanic | -.152071 .0465132 -3.27 0.001 -.2432351 -.0609069 worka | -.2501544 .0227794 -10.98 0.000 -.2948012 -.2055076 _cons | 1.37729 .1472574 9.35 0.000 1.08867 1.665909 ------------------------------------------------------------------------------ . estimates store rmodel; . lrtest urmodel rmodel; likelihood-ratio test LR chi2(3) = 520.76 (Assumption: rmodel nested in urmodel) Prob > chi2 = 0.0000 . * run logit model; . logit smoker age incomel male black hispanic > hsgrad somecol college worka; Iteration 0: log likelihood = -9171.443 Iteration 1: log likelihood = -8770.6512 Iteration 2: log likelihood = -8760.9282 Iteration 3: log likelihood = -8760.9112 Logit estimates Number of obs = 16258 LR chi2(9) = 821.06 Prob > chi2 = 0.0000 Log likelihood = -8760.9112 Pseudo R2 = 0.0448 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0026236 .0015594 -1.68 0.092 -.0056799 .0004327 incomel | -.1518663 .0251899 -6.03 0.000 -.2012376 -.102495 male | .0942472 .0390171 2.42 0.016 .0177751 .1707192 black | -.196468 .0598366 -3.28 0.001 -.3137456 -.0791904 hispanic | -.4024453 .0825043 -4.88 0.000 -.5641507 -.2407399 hsgrad | -.2906189 .0707661 -4.11 0.000 -.429318 -.1519199 somecol | -.6092455 .073822 -8.25 0.000 -.7539339 -.4645571 college | -1.325203 .0780572 -16.98 0.000 -1.478192 -1.172214 worka | -.3508271 .0389286 -9.01 0.000 -.4271257 -.2745285 _cons | 1.467936 .255991 5.73 0.000 .9662025 1.969669 ------------------------------------------------------------------------------ . * ask for marginal effects/treatment effects; . * logit model; . mfx compute; Marginal effects after logit y = Pr(smoker) (predict) = .23812502 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- age | -.000476 .00028 -1.68 0.092 -.00103 .000078 38.5474 incomel | -.0275518 .00457 -6.03 0.000 -.0365 -.018604 10.421 male*| .0171866 .00715 2.40 0.016 .003174 .0312 .39476 black*| -.0342102 .00998 -3.43 0.001 -.053765 -.014655 .111945 hispanic*| -.0661959 .01217 -5.44 0.000 -.090044 -.042347 .060709 hsgrad*| -.0513887 .01219 -4.22 0.000 -.075278 -.0275 .335527 somecol*| -.102284 .01141 -8.97 0.000 -.124644 -.079924 .268545 college*| -.2120833 .0108 -19.64 0.000 -.233248 -.190919 .329376 worka*| -.0657566 .0075 -8.76 0.000 -.080464 -.05105 .68514 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . log close; log: c:\bill\jpsm\workplace1.log log type: text closed on: 4 Nov 2004, 07:30:16 ------------------------------------------------------------------------------ Download 230.5 Kb. 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