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 5.3 on page 96 using mroz.dta.
use mroz, clear
reg educ exper expersq motheduc fatheduc huseduc if lwage ~=.
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 5, 422) = 63.30
Model | 955.830608 5 191.166122 Prob > F = 0.0000
Residual | 1274.36565 422 3.01982382 R-squared = 0.4286
-------------+------------------------------ Adj R-squared = 0.4218
Total | 2230.19626 427 5.22294206 Root MSE = 1.7378
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educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0374977 .0343102 1.09 0.275 -.0299424 .1049379
expersq | -.0006002 .0010261 -0.58 0.559 -.0026171 .0014167
motheduc | .1141532 .0307835 3.71 0.000 .0536452 .1746613
fatheduc | .1060801 .0295153 3.59 0.000 .0480648 .1640955
huseduc | .3752548 .0296347 12.66 0.000 .3170049 .4335048
_cons | 5.538311 .4597824 12.05 0.000 4.634562 6.44206
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test motheduc fatheduc huseduc
( 1) motheduc = 0
( 2) fatheduc = 0
( 3) huseduc = 0
F( 3, 422) = 104.29
Prob > F = 0.0000
ivreg lwage exper expersq (educ=fatheduc huseduc motheduc )
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 11.52
Model | 33.3927368 3 11.1309123 Prob > F = 0.0000
Residual | 189.934704 424 .447959208 R-squared = 0.1495
-------------+------------------------------ Adj R-squared = 0.1435
Total | 223.327441 427 .523015084 Root MSE = .6693
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0803918 .021774 3.69 0.000 .0375934 .1231901
exper | .0430973 .0132649 3.25 0.001 .0170242 .0691704
expersq | -.0008628 .0003962 -2.18 0.030 -.0016415 -.0000841
_cons | -.1868572 .2853959 -0.65 0.513 -.7478241 .3741097
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq fatheduc huseduc motheduc
------------------------------------------------------------------------------
reg lwage exper expersq educ
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]
-------------+----------------------------------------------------------------
exper | .0415665 .0131752 3.15 0.002 .0156697 .0674633
expersq | -.0008112 .0003932 -2.06 0.040 -.0015841 -.0000382
educ | .1074896 .0141465 7.60 0.000 .0796837 .1352956
_cons | -.5220406 .1986321 -2.63 0.009 -.9124667 -.1316144
------------------------------------------------------------------------------
Example 5.4 on page 99.
ivreg lwage exper expersq kidslt6 kidsge6 (educ=fatheduc huseduc motheduc kidslt6 kidsge6)
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 5, 422) = 7.08
Model | 33.6045837 5 6.72091675 Prob > F = 0.0000
Residual | 189.722857 422 .44958023 R-squared = 0.1505
-------------+------------------------------ Adj R-squared = 0.1404
Total | 223.327441 427 .523015084 Root MSE = .67051
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0798678 .0223575 3.57 0.000 .035922 .1238136
exper | .0414939 .0134453 3.09 0.002 .0150658 .0679221
expersq | -.0008576 .0003972 -2.16 0.031 -.0016384 -.0000768
kidslt6 | -.0313332 .0861049 -0.36 0.716 -.2005811 .1379146
kidsge6 | -.0182224 .0271427 -0.67 0.502 -.0715741 .0351292
_cons | -.1315325 .3038534 -0.43 0.665 -.7287871 .4657222
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq kidslt6 kidsge6 fatheduc huseduc motheduc
------------------------------------------------------------------------------
test kidslt6 kidsge6
( 1) kidslt6 = 0
( 2) kidsge6 = 0
F( 2, 422) = 0.31
Prob > F = 0.7368
Example 5.5 on page 106 using nls80.dta.
use nls80, clear
ivreg lwage exper tenure married south urban black educ (iq = kww )
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 935
-------------+------------------------------ F( 8, 926) = 36.96
Model | 31.4665121 8 3.93331401 Prob > F = 0.0000
Residual | 134.189771 926 .14491336 R-squared = 0.1900
-------------+------------------------------ Adj R-squared = 0.1830
Total | 165.656283 934 .177362188 Root MSE = .38067
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
iq | .0130473 .0049341 2.64 0.008 .0033641 .0227305
exper | .01442 .0033208 4.34 0.000 .0079029 .0209371
tenure | .0104562 .0026012 4.02 0.000 .0053512 .0155612
married | .2006903 .0406775 4.93 0.000 .1208595 .2805211
south | -.0515532 .0311279 -1.66 0.098 -.1126426 .0095361
urban | .1767058 .0282117 6.26 0.000 .1213394 .2320722
black | -.0225612 .0739597 -0.31 0.760 -.1677093 .1225869
educ | .0250321 .0166068 1.51 0.132 -.0075591 .0576234
_cons | 4.592453 .3257807 14.10 0.000 3.953099 5.231807
------------------------------------------------------------------------------
Instrumented: iq
Instruments: exper tenure married south urban black educ kww
------------------------------------------------------------------------------