The data files used for the examples in this text can be downloaded in a zip file from the Stata Web site. You can then use a program such as zip to unzip the data files.
Example 17.6 on page 565 using mroz.dta.
use mroz, clear
reg lwage educ exper expersq
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 26.29
Model | 35.0222967 3 11.6740989 Prob > F = 0.0000
Residual | 188.305144 424 .444115906 R-squared = 0.1568
-------------+------------------------------ Adj R-squared = 0.1509
Total | 223.327441 427 .523015084 Root MSE = .66642
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .1074896 .0141465 7.60 0.000 .0796837 .1352956
exper | .0415665 .0131752 3.15 0.002 .0156697 .0674633
expersq | -.0008112 .0003932 -2.06 0.040 -.0015841 -.0000382
_cons | -.5220406 .1986321 -2.63 0.009 -.9124667 -.1316144
------------------------------------------------------------------------------
heckman lwage educ exper expersq, select(inlf=nwifeinc educ exper expersq age kidslt6 kidsge6) twostep
Heckman selection model -- two-step estimates Number of obs = 753
(regression model with sample selection) Censored obs = 325
Uncensored obs = 428
Wald chi2(6) = 180.10
Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lwage |
educ | .1090655 .015523 7.03 0.000 .0786411 .13949
exper | .0438873 .0162611 2.70 0.007 .0120163 .0757584
expersq | -.0008591 .0004389 -1.96 0.050 -.0017194 1.15e-06
_cons | -.5781032 .3050062 -1.90 0.058 -1.175904 .019698
-------------+----------------------------------------------------------------
inlf |
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267473 1.266901
-------------+----------------------------------------------------------------
mills |
lambda | .0322619 .1336246 0.24 0.809 -.2296376 .2941613
-------------+----------------------------------------------------------------
rho | 0.04861
sigma | .66362875
lambda | .03226186 .1336246
------------------------------------------------------------------------------
heckman lwage educ exper expersq nwifeinc age kidslt6 kidsge6, select(inlf=nwifeinc educ exper ///
expersq age kidslt6 kidsge6) twostep mills(lambda)
Heckman selection model -- two-step estimates Number of obs = 753
(regression model with sample selection) Censored obs = 325
Uncensored obs = 428
Wald chi2(14) = 231.73
Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lwage |
educ | .1187171 .0340507 3.49 0.000 .051979 .1854553
exper | .0598358 .033673 1.78 0.076 -.0061621 .1258336
expersq | -.0010523 .0006381 -1.65 0.099 -.002303 .0001984
nwifeinc | .0038434 .0044919 0.86 0.392 -.0049607 .0126474
age | -.011158 .0134792 -0.83 0.408 -.0375767 .0152606
kidslt6 | -.1880451 .2308275 -0.81 0.415 -.6404586 .2643685
kidsge6 | -.0122255 .0296063 -0.41 0.680 -.0702527 .0458018
_cons | -.5602852 .4587672 -1.22 0.222 -1.459452 .3388819
-------------+----------------------------------------------------------------
inlf |
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267473 1.266901
-------------+----------------------------------------------------------------
mills |
lambda | .2884635 .4635617 0.62 0.534 -.6201008 1.197028
-------------+----------------------------------------------------------------
rho | 0.41830
sigma | .68961378
lambda | .28846351 .4635617
------------------------------------------------------------------------------
reg lambda nwifeinc educ exper expersq age kidslt6 kidsge6 if inlf
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 7, 420) = 1519.49
Model | 51.4583785 7 7.35119693 Prob > F = 0.0000
Residual | 2.0319395 420 .004837951 R-squared = 0.9620
-------------+------------------------------ Adj R-squared = 0.9614
Total | 53.490318 427 .125270066 Root MSE = .06956
------------------------------------------------------------------------------
lambda | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | .0064163 .0003463 18.53 0.000 .0057355 .007097
educ | -.0652865 .0015751 -41.45 0.000 -.0683826 -.0621904
exper | -.0663031 .0013951 -47.53 0.000 -.0690454 -.0635608
expersq | .0010572 .0000419 25.22 0.000 .0009748 .0011396
age | .0264769 .0005649 46.87 0.000 .0253666 .0275873
kidslt6 | .458195 .0092439 49.57 0.000 .4400248 .4763651
kidsge6 | -.0187996 .0029099 -6.46 0.000 -.0245193 -.0130799
_cons | .7012603 .0332077 21.12 0.000 .6359863 .7665342
------------------------------------------------------------------------------
Example 17.7 on page 568 using mroz.dta.
use mroz, clear
probit inlf nwifeinc motheduc fatheduc huseduc exper expersq age kidslt6 kidsge6
Probit estimates Number of obs = 753
LR chi2(9) = 207.10
Prob > chi2 = 0.0000
Log likelihood = -411.32238 Pseudo R2 = 0.2011
------------------------------------------------------------------------------
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0074294 .0048787 -1.52 0.128 -.0169915 .0021327
motheduc | .0295321 .0185718 1.59 0.112 -.006868 .0659322
fatheduc | .0133487 .0178491 0.75 0.455 -.0216349 .0483324
huseduc | .0161391 .019595 0.82 0.410 -.0222664 .0545446
exper | .1285092 .0185226 6.94 0.000 .0922056 .1648129
expersq | -.0019474 .0005955 -3.27 0.001 -.0031146 -.0007803
age | -.0527657 .0085423 -6.18 0.000 -.0695082 -.0360231
kidslt6 | -.8149255 .1160833 -7.02 0.000 -1.042445 -.5874063
kidsge6 | .0241511 .0432253 0.56 0.576 -.060569 .1088712
_cons | 1.146672 .4932706 2.32 0.020 .1798798 2.113465
------------------------------------------------------------------------------
predict xb, xb
gen lambda = normden(xb)/norm(xb)
ivreg lwage (educ =nwifeinc motheduc fatheduc huseduc age kidslt6 kidsge6 ) exper expersq lambda
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 4, 423) = 9.44
Model | 34.2001949 4 8.55004873 Prob > F = 0.0000
Residual | 189.127246 423 .447109329 R-squared = 0.1531
-------------+------------------------------ Adj R-squared = 0.1451
Total | 223.327441 427 .523015084 Root MSE = .66866
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0877631 .0214236 4.10 0.000 .0456531 .1298732
exper | .0457425 .0165895 2.76 0.006 .0131345 .0783505
expersq | -.0009128 .0004467 -2.04 0.042 -.0017909 -.0000347
lambda | .0404355 .1334279 0.30 0.762 -.2218287 .3026997
_cons | -.3249134 .3334547 -0.97 0.330 -.9803479 .3305212
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq lambda nwifeinc motheduc fatheduc huseduc age
kidslt6 kidsge6
------------------------------------------------------------------------------