---------------------------------------------------------------------------------------------------------------- log: C:\EC471\choice_binary.log log type: text opened on: 7 Mar 2004, 19:58:30 . regress job age school gender Source | SS df MS Number of obs = 30 -------------+------------------------------ F( 3, 26) = 4.96 Model | 2.62055889 3 .87351963 Prob > F = 0.0075 Residual | 4.57944111 26 .17613235 R-squared = 0.3640 -------------+------------------------------ Adj R-squared = 0.2906 Total | 7.2 29 .248275862 Root MSE = .41968 ------------------------------------------------------------------------------ job | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0009682 .0066986 -0.14 0.886 -.0147373 .012801 school | .0911182 .0375996 2.42 0.023 .0138312 .1684052 gender | -.3803107 .1562374 -2.43 0.022 -.7014612 -.0591602 _cons | -.2227047 .6153338 -0.36 0.720 -1.487541 1.042132 ------------------------------------------------------------------------------ . predict pred_lpm, xb . probit job age school gender Iteration 0: log likelihood = -20.19035 Iteration 1: log likelihood = -13.937494 Iteration 2: log likelihood = -13.305501 Iteration 3: log likelihood = -13.267639 Iteration 4: log likelihood = -13.26742 Probit estimates Number of obs = 30 LR chi2(3) = 13.85 Prob > chi2 = 0.0031 Log likelihood = -13.26742 Pseudo R2 = 0.3429 ------------------------------------------------------------------------------ job | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0061331 .0237865 -0.26 0.797 -.0527538 .0404876 school | .3869652 .1759277 2.20 0.028 .0421532 .7317773 gender | -1.45382 .6299747 -2.31 0.021 -2.688547 -.2190919 _cons | -3.047916 2.399211 -1.27 0.204 -7.750285 1.654452 ------------------------------------------------------------------------------ . predict pred_probit, p . dprobit job age school gender Iteration 0: log likelihood = -20.19035 Iteration 1: log likelihood = -13.937494 Iteration 2: log likelihood = -13.305501 Iteration 3: log likelihood = -13.267639 Iteration 4: log likelihood = -13.26742 Probit estimates Number of obs = 30 LR chi2(3) = 13.85 Prob > chi2 = 0.0031 Log likelihood = -13.26742 Pseudo R2 = 0.3429 ------------------------------------------------------------------------------ job | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ] ---------+-------------------------------------------------------------------- age | -.0022073 .0085406 -0.26 0.797 41.3333 -.018947 .014532 school | .1392695 .0601222 2.20 0.028 11.8333 .021432 .257107 gender*| -.4692287 .1628219 -2.31 0.021 .566667 -.788354 -.150104 ---------+-------------------------------------------------------------------- obs. P | .6 pred. P | .675028 (at x-bar) ------------------------------------------------------------------------------ (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>|z| are the test of the underlying coefficient being 0 . logit job age school gender Iteration 0: log likelihood = -20.19035 Iteration 1: log likelihood = -14.08727 Iteration 2: log likelihood = -13.333675 Iteration 3: log likelihood = -13.246511 Iteration 4: log likelihood = -13.244612 Iteration 5: log likelihood = -13.244611 Logit estimates Number of obs = 30 LR chi2(3) = 13.89 Prob > chi2 = 0.0031 Log likelihood = -13.244611 Pseudo R2 = 0.3440 ------------------------------------------------------------------------------ job | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0098882 .0389641 -0.25 0.800 -.0862564 .06648 school | .6741644 .3215752 2.10 0.036 .0438886 1.30444 gender | -2.608477 1.199745 -2.17 0.030 -4.959935 -.2570198 _cons | -5.266331 4.120738 -1.28 0.201 -13.34283 2.810168 ------------------------------------------------------------------------------ . predict pred_logit, p . logistic job age school gender Logistic regression Number of obs = 30 LR chi2(3) = 13.89 Prob > chi2 = 0.0031 Log likelihood = -13.244611 Pseudo R2 = 0.3440 ------------------------------------------------------------------------------ job | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .9901606 .0385807 -0.25 0.800 .917359 1.06874 school | 1.962392 .6310567 2.10 0.036 1.044866 3.685625 gender | .0736466 .0883572 -2.17 0.030 .0070134 .7733529 ------------------------------------------------------------------------------ . predict pred_logistic, p . edit - preserve . logit Logit estimates Number of obs = 30 LR chi2(3) = 13.89 Prob > chi2 = 0.0031 Log likelihood = -13.244611 Pseudo R2 = 0.3440 ------------------------------------------------------------------------------ job | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0098882 .0389641 -0.25 0.800 -.0862564 .06648 school | .6741644 .3215752 2.10 0.036 .0438886 1.30444 gender | -2.608477 1.199745 -2.17 0.030 -4.959935 -.2570198 _cons | -5.266331 4.120738 -1.28 0.201 -13.34283 2.810168 ------------------------------------------------------------------------------ **** We need to install dlogit2 for marginal effects of the logit estimates . Help - search (all) - dlogit2 - install . dlogit2 job age school gender Marginal effects from logit Number of obs = 30 chi2(3) = 8.83 Prob > chi2 = 0.0316 Log Likelihood = -13.244611 Pseudo R2 = 0.3440 ------------------------------------------------------------------------------ job | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | -.0020954 .0082105 -0.26 0.799 -.0181877 .0139969 school | .1428596 .062976 2.27 0.023 .0194288 .2662903 gender | -.5527524 .2267991 -2.44 0.015 -.9972705 -.1082343 _cons | -1.115968 .8833022 -1.26 0.206 -2.847208 .6152726 ------------------------------------------------------------------------------ Marginal effects evaluated at age school gender _cons x 41.33333 11.83333 .5666667 1 . save "C:\EC471\choice_binary1.dta" file C:\EC471\choice_binary1.dta saved list pred_lpm pred_probit pred_logit pred_logistic +-------------------------------------------+ | pred_lpm pred_p~t pred_l~t pred_l~c | |-------------------------------------------| 1. | 1.205173 .9984285 .9945883 .9945883 | 2. | .6397215 .7602951 .7733448 .7733448 | 3. | .8151807 .9248186 .9245898 .9245898 | 4. | .532491 .5095153 .5346048 .5346048 | 5. | .8465095 .9253966 .9293296 .9293296 | |-------------------------------------------| 6. | .4342487 .4153232 .4113591 .4113591 | 7. | 1.009382 .9818525 .9765006 .9765006 | 8. | .6352275 .6859546 .7173721 .7173721 | 9. | .8290821 .9085606 .9167091 .9167091 | 10. | .452991 .3860938 .3972671 .3972671 | |-------------------------------------------| 11. | .7553912 .8543728 .8701486 .8701486 | 12. | 1.013255 .9829184 .9773914 .9773914 | 13. | .4303759 .4057835 .4018164 .4018164 | 14. | .5321442 .5855156 .5950862 .5950862 | 15. | .1792894 .1041526 .1003838 .1003838 | |-------------------------------------------| 16. | .6545911 .7281004 .7556962 .7556962 | 17. | .6339124 .7487157 .7627772 .7627772 | 18. | .266535 .185178 .1738827 .1738827 | 19. | .4545805 .4660999 .4623967 .4623967 | 20. | .9356914 .9653944 .961978 .961978 | |-------------------------------------------| 21. | 1.025841 .9860208 .9800642 .9800642 | 22. | .4439305 .4393799 .4354945 .4354945 | 23. | -.029088 .013983 .0217048 .0217048 | 24. | .356685 .3032854 .2902687 .2902687 | 25. | .4700715 .5052068 .5018782 .5018782 | |-------------------------------------------| 26. | .2694395 .1901327 .1781852 .1781852 | 27. | 1.104373 .9938793 .9883499 .9883499 | 28. | .2858986 .2197184 .204144 .204144 | 29. | .3692714 .3316926 .3174461 .3174461 | 30. | .4478032 .4490708 .4452417 .4452417 | +-------------------------------------------+ . . log close log: C:\EC471\choice_binary.log log type: text closed on: 7 Mar 2004, 20:16:47 --------------------------------------------------------------------------------------------------------------