Probit, Logit & Tobit Methods – Lecture 12 Stata Output - Faculty-Staff
Probit, Logit & Tobit Methods – Lecture 12 Stata Output - Faculty-Staff
Probit, Logit & Tobit Methods – Lecture 12 Stata Output - Faculty-Staff
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<strong>Probit</strong>, <strong>Logit</strong> & <strong>Tobit</strong> <strong>Methods</strong> <strong>–</strong> <strong>Lecture</strong> <strong>12</strong><br />
<strong>Stata</strong> <strong>Output</strong><br />
. *<br />
. clear<br />
. use Mroz.dta<br />
. *<br />
. * Linear Probability Model<br />
. *<br />
. reg inlf nwifeinc educ exper expersq<br />
Source | SS df MS Number of obs = 753<br />
-------------+------------------------------ F( 4, 748) = 41.34<br />
Model | 33.4436821 4 8.36092052 Prob > F = 0.0000<br />
Residual | 151.284074 748 .202251435 R-squared = 0.1810<br />
-------------+------------------------------ Adj R-squared = 0.1767<br />
Total | 184.727756 752 .245648611 Root MSE = .44972<br />
------------------------------------------------------------------------------<br />
inlf | Coef. Std. Err. t P>|t| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
nwifeinc | -.0049579 .0014974 -3.31 0.001 -.0078976 -.0020182<br />
educ | .0395919 .0075882 5.22 0.000 .0246951 .0544887<br />
exper | .044751 .0059173 7.56 0.000 .0331345 .0563675<br />
expersq | -.0008855 .0001902 -4.66 0.000 -.00<strong>12</strong>589 -.0005<strong>12</strong>2<br />
_cons | -.1363468 .0928188 -1.47 0.142 -.3185631 .0458695<br />
------------------------------------------------------------------------------<br />
. mfx<br />
Marginal effects after regress<br />
y = Fitted values (predict)<br />
= .56839309<br />
------------------------------------------------------------------------------<br />
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X<br />
---------+--------------------------------------------------------------------<br />
nwifeinc | -.0049579 .0015 -3.31 0.001 -.007893 -.002023 20.<strong>12</strong>9<br />
educ | .0395919 .00759 5.22 0.000 .024719 .054465 <strong>12</strong>.2869<br />
exper | .044751 .00592 7.56 0.000 .033153 .056349 10.6308<br />
expersq | -.0008855 .00019 -4.66 0.000 -.00<strong>12</strong>58 -.000513 178.039<br />
------------------------------------------------------------------------------
. *<br />
. * <strong>Probit</strong>.<br />
. *<br />
. probit inlf nwifeinc educ exper expersq<br />
Iteration 0: log likelihood = -514.8732<br />
Iteration 1: log likelihood = -442.72285<br />
Iteration 2: log likelihood = -441.73544<br />
Iteration 3: log likelihood = -441.73476<br />
<strong>Probit</strong> estimates Number of obs = 753<br />
LR chi2(4) = 146.28<br />
Prob > chi2 = 0.0000<br />
Log likelihood = -441.73476 Pseudo R2 = 0.1421<br />
------------------------------------------------------------------------------<br />
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
nwifeinc | -.0148638 .0045696 -3.25 0.001 -.0238201 -.0059075<br />
educ | .<strong>12</strong>11665 .0235956 5.14 0.000 .0749199 .167413<br />
exper | .<strong>12</strong>31042 .01799 6.84 0.000 .0878444 .158364<br />
expersq | -.0023743 .0005848 -4.06 0.000 -.0035205 -.00<strong>12</strong>281<br />
_cons | -1.880013 .29057 -6.47 0.000 -2.449519 -1.310506<br />
------------------------------------------------------------------------------<br />
. mfx<br />
Marginal effects after probit<br />
y = Pr(inlf) (predict)<br />
= .57751056<br />
------------------------------------------------------------------------------<br />
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X<br />
---------+--------------------------------------------------------------------<br />
nwifeinc | -.0058175 .00179 -3.25 0.001 -.009323 -.0023<strong>12</strong> 20.<strong>12</strong>9<br />
educ | .0474232 .00923 5.14 0.000 .029337 .065509 <strong>12</strong>.2869<br />
exper | .0481816 .00706 6.82 0.000 .034341 .062022 10.6308<br />
expersq | -.0009293 .00023 -4.05 0.000 -.001379 -.00048 178.039<br />
------------------------------------------------------------------------------<br />
. *<br />
. * <strong>Logit</strong>.<br />
. *<br />
. logit inlf nwifeinc educ exper expersq<br />
Iteration 0: log likelihood = -514.8732<br />
Iteration 1: log likelihood = -443.0279<br />
Iteration 2: log likelihood = -441.62707<br />
Iteration 3: log likelihood = -441.6214<br />
<strong>Logit</strong> estimates Number of obs = 753<br />
LR chi2(4) = 146.50<br />
Prob > chi2 = 0.0000<br />
Log likelihood = -441.6214 Pseudo R2 = 0.1423<br />
------------------------------------------------------------------------------<br />
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
nwifeinc | -.0254526 .0077905 -3.27 0.001 -.0407217 -.0101836<br />
educ | .2032535 .0402968 5.04 0.000 .<strong>12</strong>42733 .2822337<br />
exper | .2014209 .0301041 6.69 0.000 .1424179 .2604239<br />
expersq | -.00386 .0009793 -3.94 0.000 -.0057794 -.0019406<br />
_cons | -3.117439 .4967336 -6.28 0.000 -4.091019 -2.143859<br />
------------------------------------------------------------------------------
. mfx<br />
Marginal effects after logit<br />
y = Pr(inlf) (predict)<br />
= .57971549<br />
------------------------------------------------------------------------------<br />
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X<br />
---------+--------------------------------------------------------------------<br />
nwifeinc | -.0062014 .0019 -3.27 0.001 -.009922 -.002481 20.<strong>12</strong>9<br />
educ | .0495218 .0098 5.05 0.000 .030305 .068738 <strong>12</strong>.2869<br />
exper | .0490753 .00737 6.66 0.000 .034634 .063517 10.6308<br />
expersq | -.0009405 .00024 -3.93 0.000 -.00141 -.000471 178.039<br />
------------------------------------------------------------------------------<br />
. tobit faminc educ exper expersq, ul(100000)<br />
<strong>Tobit</strong> estimates Number of obs = 753<br />
LR chi2(3) = 107.68<br />
Prob > chi2 = 0.0000<br />
Log likelihood = -8098.6347 Pseudo R2 = 0.0066<br />
------------------------------------------------------------------------------<br />
faminc | Coef. Std. Err. t P>|t| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
educ | 1945.447 182.8746 10.64 0.000 1586.44 2304.454<br />
exper | -48.4024 148.6118 -0.33 0.745 -340.1469 243.3421<br />
expersq | -1.02987 4.794605 -0.21 0.830 -10.44231 8.382573<br />
_cons | -<strong>12</strong>4.9<strong>12</strong>9 2332.854 -0.05 0.957 -4704.614 4454.788<br />
-------------+----------------------------------------------------------------<br />
_se | 11341.46 292.2513 (Ancillary parameter)<br />
------------------------------------------------------------------------------<br />
Obs. summary: 753 uncensored observations
Heckman Correction <strong>–</strong> <strong>Lecture</strong> <strong>12</strong><br />
<strong>Stata</strong> <strong>Output</strong><br />
. logit inlf nwifeinc educ exper expersq age kidslt6 kidsge6<br />
Iteration 0: log likelihood = -514.8732<br />
Iteration 1: log likelihood = -406.94<strong>12</strong>3<br />
Iteration 2: log likelihood = -401.85151<br />
Iteration 3: log likelihood = -401.76519<br />
Iteration 4: log likelihood = -401.76515<br />
<strong>Logit</strong> estimates Number of obs = 753<br />
LR chi2(7) = 226.22<br />
Prob > chi2 = 0.0000<br />
Log likelihood = -401.76515 Pseudo R2 = 0.2197<br />
------------------------------------------------------------------------------<br />
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
nwifeinc | -.0213452 .0084214 -2.53 0.011 -.0378509 -.0048394<br />
educ | .2211704 .0434396 5.09 0.000 .1360303 .3063105<br />
exper | .2058695 .0320569 6.42 0.000 .1430391 .2686999<br />
expersq | -.0031541 .0010161 -3.10 0.002 -.0051456 -.0011626<br />
age | -.0880244 .014573 -6.04 0.000 -.116587 -.0594618<br />
kidslt6 | -1.443354 .2035849 -7.09 0.000 -1.842373 -1.044335<br />
kidsge6 | .0601<strong>12</strong>2 .0747897 0.80 0.422 -.086473 .2066974<br />
_cons | .4254524 .8603696 0.49 0.621 -1.260841 2.111746<br />
------------------------------------------------------------------------------<br />
. predict xb, xb<br />
. gen smallphi=normd(xb)<br />
. gen largephi=normprob(xb)<br />
. gen lambda=smallphi/largephi<br />
. reg lwage educ exper expersq lambda if inlf==1<br />
Source | SS df MS Number of obs = 428<br />
-------------+------------------------------ F( 4, 423) = 19.71<br />
Model | 35.09243 4 8.77310751 Prob > F = 0.0000<br />
Residual | 188.235021 423 .44500005 R-squared = 0.1571<br />
-------------+------------------------------ Adj R-squared = 0.1492<br />
Total | 223.327451 427 .523015108 Root MSE = .66708<br />
------------------------------------------------------------------------------<br />
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
educ | .1097902 .0153005 7.18 0.000 .0797157 .1398648<br />
exper | .0451776 .01602<strong>12</strong> 2.82 0.005 .0136865 .0766687<br />
expersq | -.0008865 .0004369 -2.03 0.043 -.0017453 -.0000277<br />
lambda | .034491 .0868842 0.40 0.692 -.1362876 .2052696<br />
_cons | -.5965943 .2735022 -2.18 0.030 -1.134187 -.0590017<br />
------------------------------------------------------------------------------
. heckman lwage educ exper expersq, select(inlf= nwifeinc educ exper expersq<br />
age kidslt6 kidsge<br />
> 6) twostep<br />
Heckman selection model -- two-step estimates Number of obs = 753<br />
(regression model with sample selection) Censored obs = 325<br />
Uncensored obs = 428<br />
Wald chi2(6) = 180.10<br />
Prob > chi2 = 0.0000<br />
------------------------------------------------------------------------------<br />
| Coef. Std. Err. z P>|z| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
lwage |<br />
educ | .1090655 .015523 7.03 0.000 .0786411 .13949<br />
exper | .0438873 .0162611 2.70 0.007 .0<strong>12</strong>0163 .0757584<br />
expersq | -.0008591 .0004389 -1.96 0.050 -.0017194 1.15e-06<br />
_cons | -.5781033 .3050062 -1.90 0.058 -1.175904 .0196979<br />
-------------+----------------------------------------------------------------<br />
inlf |<br />
nwifeinc | -.0<strong>12</strong>0237 .0048398 -2.48 0.013 -.0215096 -.0025378<br />
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402<br />
exper | .<strong>12</strong>33476 .0187164 6.59 0.000 .0866641 .1600311<br />
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111<br />
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376<br />
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029<br />
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .<strong>12</strong><strong>12</strong>179<br />
_cons | .2700768 .508593 0.53 0.595 -.7267472 1.266901<br />
-------------+----------------------------------------------------------------<br />
mills |<br />
lambda | .0322619 .1336246 0.24 0.809 -.2296376 .2941613<br />
-------------+----------------------------------------------------------------<br />
rho | 0.04861<br />
sigma | .66362876<br />
lambda | .03226186 .1336246<br />
------------------------------------------------------------------------------
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