Example 15.1: Estimating the Return to Education for Married Women
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz
reg lwage educ
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 1, 426) = 56.93
Model | 26.3264237 1 26.3264237 Prob > F = 0.0000
Residual | 197.001028 426 .462443727 R-squared = 0.1179
-------------+------------------------------ Adj R-squared = 0.1158
Total | 223.327451 427 .523015108 Root MSE = .68003
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .1086487 .0143998 7.55 0.000 .0803451 .1369523
_cons | -.1851969 .1852259 -1.00 0.318 -.5492674 .1788735
------------------------------------------------------------------------------
ivreg lwage (educ = fatheduc )
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 1, 426) = 2.84
Model | 20.8673618 1 20.8673618 Prob > F = 0.0929
Residual | 202.460089 426 .475258426 R-squared = 0.0934
-------------+------------------------------ Adj R-squared = 0.0913
Total | 223.327451 427 .523015108 Root MSE = .68939
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0591735 .0351418 1.68 0.093 -.0098994 .1282463
_cons | .4411035 .4461018 0.99 0.323 -.4357311 1.317938
------------------------------------------------------------------------------
Instrumented: educ
Instruments: fatheduc
------------------------------------------------------------------------------
Example 15.2: Estimating the Return to Education for Men
use http://fmwww.bc.edu/ec-p/data/wooldridge/wage2
ivreg lwage (educ = sibs )
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 935
-------------+------------------------------ F( 1, 933) = 21.59
Model | -1.5197389 1 -1.5197389 Prob > F = 0.0000
Residual | 167.176033 933 .179181172 R-squared = .
-------------+------------------------------ Adj R-squared = .
Total | 165.656294 934 .177362199 Root MSE = .4233
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .1224327 .0263506 4.65 0.000 .0707194 .1741459
_cons | 5.130026 .3551712 14.44 0.000 4.432999 5.827053
------------------------------------------------------------------------------
Instrumented: educ
Instruments: sibs
------------------------------------------------------------------------------
reg lwage educ
Source | SS df MS Number of obs = 935
-------------+------------------------------ F( 1, 933) = 100.70
Model | 16.1377074 1 16.1377074 Prob > F = 0.0000
Residual | 149.518587 933 .16025572 R-squared = 0.0974
-------------+------------------------------ Adj R-squared = 0.0964
Total | 165.656294 934 .177362199 Root MSE = .40032
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0598392 .0059631 10.03 0.000 .0481366 .0715418
_cons | 5.973062 .0813737 73.40 0.000 5.813366 6.132759
------------------------------------------------------------------------------
Example 15.3: Estimating the Effect of Smoking on Birth Weight
use http://fmwww.bc.edu/ec-p/data/wooldridge/bwght
ivreg lbwght (packs = cigprice ), first
First-stage regressions
-----------------------
Source | SS df MS Number of obs = 1388
-------------+------------------------------ F( 1, 1386) = 0.13
Model | .011648626 1 .011648626 Prob > F = 0.7179
Residual | 123.684481 1386 .089238442 R-squared = 0.0001
-------------+------------------------------ Adj R-squared = -0.0006
Total | 123.696129 1387 .089182501 Root MSE = .29873
------------------------------------------------------------------------------
packs | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cigprice | .0002829 .000783 0.36 0.718 -.0012531 .0018188
_cons | .0674257 .1025384 0.66 0.511 -.1337215 .2685728
------------------------------------------------------------------------------
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 1388
-------------+------------------------------ F( 1, 1386) = 0.12
Model | -1171.28083 1 -1171.28083 Prob > F = 0.7312
Residual | 1221.70115 1386 .881458263 R-squared = .
-------------+------------------------------ Adj R-squared = .
Total | 50.4203246 1387 .036352073 Root MSE = .93886
------------------------------------------------------------------------------
lbwght | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
packs | 2.988674 8.698884 0.34 0.731 -14.07573 20.05307
_cons | 4.448137 .9081547 4.90 0.000 2.66663 6.229643
------------------------------------------------------------------------------
Instrumented: packs
Instruments: cigprice
------------------------------------------------------------------------------
Example 15.4: Using College Proximity as an IV for Education
use http://fmwww.bc.edu/ec-p/data/wooldridge/card
ivreg lwage (educ = nearc4 ) exper expersq black smsa south, first
First-stage regressions
-----------------------
Source | SS df MS Number of obs = 3010
-------------+------------------------------ F( 6, 3003) = 451.87
Model | 10230.4843 6 1705.08072 Prob > F = 0.0000
Residual | 11331.5958 3003 3.77342516 R-squared = 0.4745
-------------+------------------------------ Adj R-squared = 0.4734
Total | 21562.0801 3009 7.16586243 Root MSE = 1.9425
------------------------------------------------------------------------------
educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.4100081 .0336939 -12.17 0.000 -.4760735 -.3439427
expersq | .0007323 .0016499 0.44 0.657 -.0025029 .0039674
black | -1.006138 .0896454 -11.22 0.000 -1.181911 -.8303656
smsa | .4038769 .0848872 4.76 0.000 .2374339 .5703199
south | -.291464 .0792247 -3.68 0.000 -.4468042 -.1361238
nearc4 | .3373208 .0825004 4.09 0.000 .1755577 .4990839
_cons | 16.65917 .1763889 94.45 0.000 16.31332 17.00503
------------------------------------------------------------------------------
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 3010
-------------+------------------------------ F( 6, 3003) = 120.83
Model | 133.463217 6 22.2438695 Prob > F = 0.0000
Residual | 459.178394 3003 .152906558 R-squared = 0.2252
-------------+------------------------------ Adj R-squared = 0.2237
Total | 592.641611 3009 .196956335 Root MSE = .39103
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .1322888 .0492332 2.69 0.007 .0357545 .228823
exper | .107498 .0213006 5.05 0.000 .0657327 .1492632
expersq | -.0022841 .0003341 -6.84 0.000 -.0029392 -.0016289
black | -.130802 .0528723 -2.47 0.013 -.2344716 -.0271324
smsa | .1313237 .0301298 4.36 0.000 .0722465 .1904009
south | -.1049005 .0230731 -4.55 0.000 -.1501412 -.0596599
_cons | 3.752783 .8293408 4.53 0.000 2.126649 5.378916
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq black smsa south nearc4
------------------------------------------------------------------------------
reg lwage educ exper expersq black smsa south
Source | SS df MS Number of obs = 3010
-------------+------------------------------ F( 6, 3003) = 204.93
Model | 172.165615 6 28.6942691 Prob > F = 0.0000
Residual | 420.475997 3003 .140018647 R-squared = 0.2905
-------------+------------------------------ Adj R-squared = 0.2891
Total | 592.641611 3009 .196956335 Root MSE = .37419
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .074009 .0035054 21.11 0.000 .0671357 .0808823
exper | .0835958 .0066478 12.57 0.000 .0705612 .0966305
expersq | -.0022409 .0003178 -7.05 0.000 -.0028641 -.0016177
black | -.1896316 .0176266 -10.76 0.000 -.2241929 -.1550702
smsa | .161423 .0155733 10.37 0.000 .1308876 .1919583
south | -.1248615 .0151182 -8.26 0.000 -.1545046 -.0952184
_cons | 4.733664 .0676026 70.02 0.000 4.601112 4.866217
------------------------------------------------------------------------------
Example 15.5: Return to Education for Working Women
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz
reg educ exper expersq motheduc fatheduc
Source | SS df MS Number of obs = 753
-------------+------------------------------ F( 4, 748) = 66.52
Model | 1025.94324 4 256.48581 Prob > F = 0.0000
Residual | 2884.0966 748 3.85574412 R-squared = 0.2624
-------------+------------------------------ Adj R-squared = 0.2584
Total | 3910.03984 752 5.19952106 Root MSE = 1.9636
------------------------------------------------------------------------------
educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .085378 .0255485 3.34 0.001 .0352228 .1355333
expersq | -.0018564 .0008276 -2.24 0.025 -.0034812 -.0002317
motheduc | .1856173 .0259869 7.14 0.000 .1346014 .2366331
fatheduc | .1845745 .0244979 7.53 0.000 .1364817 .2326674
_cons | 8.366716 .2667111 31.37 0.000 7.843125 8.890307
------------------------------------------------------------------------------
test motheduc fatheduc
( 1) motheduc = 0.0
( 2) fatheduc = 0.0
F( 2, 748) = 124.76
Prob > F = 0.0000
ivreg lwage (educ = motheduc fatheduc) exper expersq
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 8.14
Model | 30.3074295 3 10.1024765 Prob > F = 0.0000
Residual | 193.020022 424 .4552359 R-squared = 0.1357
-------------+------------------------------ Adj R-squared = 0.1296
Total | 223.327451 427 .523015108 Root MSE = .67471
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0613966 .0314367 1.95 0.051 -.0003945 .1231878
exper | .0441704 .0134325 3.29 0.001 .0177679 .0705729
expersq | -.000899 .0004017 -2.24 0.026 -.0016885 -.0001094
_cons | .0481003 .4003281 0.12 0.904 -.7387744 .834975
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq motheduc fatheduc
------------------------------------------------------------------------------
Example 15.6: Using Two Test Scores as Indicators of Ability
use http://fmwww.bc.edu/ec-p/data/wooldridge/wage2
ivreg lwage educ exper tenure married south urban black (IQ =KWW)
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 935
-------------+------------------------------ F( 8, 926) = 36.96
Model | 31.4665073 8 3.93331341 Prob > F = 0.0000
Residual | 134.189787 926 .144913377 R-squared = 0.1900
-------------+------------------------------ Adj R-squared = 0.1830
Total | 165.656294 934 .177362199 Root MSE = .38067
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
IQ | .0130473 .0049341 2.64 0.008 .0033641 .0227305
educ | .0250321 .0166068 1.51 0.132 -.0075591 .0576234
exper | .01442 .0033208 4.34 0.000 .0079029 .0209371
tenure | .0104562 .0026012 4.02 0.000 .0053512 .0155612
married | .2006904 .0406775 4.93 0.000 .1208596 .2805212
south | -.0515532 .0311279 -1.66 0.098 -.1126426 .0095362
urban | .1767058 .0282117 6.26 0.000 .1213394 .2320722
black | -.0225611 .0739597 -0.31 0.760 -.1677092 .122587
_cons | 4.592453 .3257807 14.10 0.000 3.953099 5.231807
------------------------------------------------------------------------------
Instrumented: IQ
Instruments: educ exper tenure married south urban black KWW
------------------------------------------------------------------------------
Example 15.7: Return to Education for Working Women
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz
reg educ exper expersq motheduc fatheduc if lwage<.
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 4, 423) = 28.36
Model | 471.620998 4 117.90525 Prob > F = 0.0000
Residual | 1758.57526 423 4.15738833 R-squared = 0.2115
-------------+------------------------------ Adj R-squared = 0.2040
Total | 2230.19626 427 5.22294206 Root MSE = 2.039
------------------------------------------------------------------------------
educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0452254 .0402507 1.12 0.262 -.0338909 .1243417
expersq | -.0010091 .0012033 -0.84 0.402 -.0033744 .0013562
motheduc | .157597 .0358941 4.39 0.000 .087044 .2281501
fatheduc | .1895484 .0337565 5.62 0.000 .1231971 .2558997
_cons | 9.10264 .4265614 21.34 0.000 8.264196 9.941084
------------------------------------------------------------------------------
predict double uhat1, res
reg lwage educ exper expersq uhat1
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 4, 423) = 20.50
Model | 36.2573159 4 9.06432898 Prob > F = 0.0000
Residual | 187.070135 423 .442246183 R-squared = 0.1624
-------------+------------------------------ Adj R-squared = 0.1544
Total | 223.327451 427 .523015108 Root MSE = .66502
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0613966 .0309849 1.98 0.048 .000493 .1223003
exper | .0441704 .0132394 3.34 0.001 .0181471 .0701937
expersq | -.000899 .0003959 -2.27 0.024 -.0016772 -.0001208
uhat1 | .0581666 .0348073 1.67 0.095 -.0102501 .1265834
_cons | .0481003 .3945753 0.12 0.903 -.7274721 .8236727
------------------------------------------------------------------------------
reg lwage educ exper expersq
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 26.29
Model | 35.0223023 3 11.6741008 Prob > F = 0.0000
Residual | 188.305149 424 .444115917 R-squared = 0.1568
-------------+------------------------------ Adj R-squared = 0.1509
Total | 223.327451 427 .523015108 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 | -.5220407 .1986321 -2.63 0.009 -.9124668 -.1316145
------------------------------------------------------------------------------
ivreg lwage (educ = motheduc fatheduc) exper expersq
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 8.14
Model | 30.3074295 3 10.1024765 Prob > F = 0.0000
Residual | 193.020022 424 .4552359 R-squared = 0.1357
-------------+------------------------------ Adj R-squared = 0.1296
Total | 223.327451 427 .523015108 Root MSE = .67471
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0613966 .0314367 1.95 0.051 -.0003945 .1231878
exper | .0441704 .0134325 3.29 0.001 .0177679 .0705729
expersq | -.000899 .0004017 -2.24 0.026 -.0016885 -.0001094
_cons | .0481003 .4003281 0.12 0.904 -.7387744 .834975
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq motheduc fatheduc
------------------------------------------------------------------------------
Example 15.8: Return to Education for Working Women
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz
ivreg lwage (educ = motheduc fatheduc) exper expersq
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 8.14
Model | 30.3074295 3 10.1024765 Prob > F = 0.0000
Residual | 193.020022 424 .4552359 R-squared = 0.1357
-------------+------------------------------ Adj R-squared = 0.1296
Total | 223.327451 427 .523015108 Root MSE = .67471
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0613966 .0314367 1.95 0.051 -.0003945 .1231878
exper | .0441704 .0134325 3.29 0.001 .0177679 .0705729
expersq | -.000899 .0004017 -2.24 0.026 -.0016885 -.0001094
_cons | .0481003 .4003281 0.12 0.904 -.7387744 .834975
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq motheduc fatheduc
------------------------------------------------------------------------------
ssc install overid, replace
overid
Test of overidentifying restrictions: .378071 Chi-sq( 1) P-value = .5386
ivreg lwage (educ = motheduc fatheduc huseduc) exper expersq
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 11.52
Model | 33.3927427 3 11.1309142 Prob > F = 0.0000
Residual | 189.934709 424 .447959218 R-squared = 0.1495
-------------+------------------------------ Adj R-squared = 0.1435
Total | 223.327451 427 .523015108 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 | -.1868574 .2853959 -0.65 0.513 -.7478243 .3741096
------------------------------------------------------------------------------
Instrumented: educ
Instruments: exper expersq motheduc fatheduc huseduc
------------------------------------------------------------------------------
overid
Test of overidentifying restrictions: 1.115043 Chi-sq( 2) P-value = .5726
Example 15.9: Return of Education to Fertility
use http://fmwww.bc.edu/ec-p/data/wooldridge/fertil1
ivreg kids (educ = meduc feduc) age agesq black east northcen west farm othrural town smcity y74 y76 y78 y80 y82 y84
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 1129
-------------+------------------------------ F( 17, 1111) = 7.72
Model | 395.36632 17 23.2568424 Prob > F = 0.0000
Residual | 2690.14298 1111 2.42137082 R-squared = 0.1281
-------------+------------------------------ Adj R-squared = 0.1148
Total | 3085.5093 1128 2.73538059 Root MSE = 1.5561
------------------------------------------------------------------------------
kids | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.1527395 .0392232 -3.89 0.000 -.2296993 -.0757796
age | .5235536 .1390348 3.77 0.000 .2507532 .796354
agesq | -.005716 .0015705 -3.64 0.000 -.0087976 -.0026345
black | 1.072952 .1737155 6.18 0.000 .732105 1.4138
east | .2285554 .1338537 1.71 0.088 -.0340792 .49119
northcen | .3744188 .122061 3.07 0.002 .1349228 .6139148
west | .2076398 .1676568 1.24 0.216 -.1213199 .5365995
farm | -.0770015 .1513718 -0.51 0.611 -.3740083 .2200052
othrural | -.1952451 .181551 -1.08 0.282 -.5514666 .1609764
town | .08181 .1246821 0.66 0.512 -.162829 .3264489
smcity | .2124996 .160425 1.32 0.186 -.1022706 .5272698
y74 | .2721292 .172944 1.57 0.116 -.0672045 .6114629
y76 | -.0945483 .1792324 -0.53 0.598 -.4462205 .2571239
y78 | -.0572543 .1825536 -0.31 0.754 -.415443 .3009343
y80 | -.053248 .1847175 -0.29 0.773 -.4156825 .3091865
y82 | -.4962149 .1765888 -2.81 0.005 -.8427 -.1497298
y84 | -.5213604 .1779205 -2.93 0.003 -.8704586 -.1722623
_cons | -7.241244 3.136642 -2.31 0.021 -13.39565 -1.086834
------------------------------------------------------------------------------
Instrumented: educ
Instruments: age agesq black east northcen west farm othrural town smcity y74
y76 y78 y80 y82 y84 meduc feduc
------------------------------------------------------------------------------
reg kids educ age agesq black east northcen west farm othrural town smcity y74 y76 y78 y80 y82 y84
Source | SS df MS Number of obs = 1129
-------------+------------------------------ F( 17, 1111) = 9.72
Model | 399.610888 17 23.5065228 Prob > F = 0.0000
Residual | 2685.89841 1111 2.41755033 R-squared = 0.1295
-------------+------------------------------ Adj R-squared = 0.1162
Total | 3085.5093 1128 2.73538059 Root MSE = 1.5548
------------------------------------------------------------------------------
kids | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.1284268 .0183486 -7.00 0.000 -.1644286 -.092425
age | .5321346 .1383863 3.85 0.000 .2606065 .8036626
agesq | -.005804 .0015643 -3.71 0.000 -.0088733 -.0027347
black | 1.075658 .1735356 6.20 0.000 .7351631 1.416152
east | .217324 .1327878 1.64 0.102 -.0432192 .4778672
northcen | .363114 .1208969 3.00 0.003 .125902 .6003261
west | .1976032 .1669134 1.18 0.237 -.1298978 .5251041
farm | -.0525575 .14719 -0.36 0.721 -.3413592 .2362443
othrural | -.1628537 .175442 -0.93 0.353 -.5070887 .1813814
town | .0843532 .124531 0.68 0.498 -.1599893 .3286957
smcity | .2118791 .160296 1.32 0.187 -.1026379 .5263961
y74 | .2681825 .172716 1.55 0.121 -.0707039 .6070689
y76 | -.0973795 .1790456 -0.54 0.587 -.448685 .2539261
y78 | -.0686665 .1816837 -0.38 0.706 -.4251483 .2878154
y80 | -.0713053 .1827707 -0.39 0.697 -.42992 .2873093
y82 | -.5224842 .1724361 -3.03 0.003 -.8608214 -.184147
y84 | -.5451661 .1745162 -3.12 0.002 -.8875846 -.2027477
_cons | -7.742457 3.051767 -2.54 0.011 -13.73033 -1.754579
------------------------------------------------------------------------------
reg educ meduc feduc age agesq black east northcen west farm othrural town smcity y74 y76 y78 y80 y82 y84
Source | SS df MS Number of obs = 1129
-------------+------------------------------ F( 18, 1110) = 24.82
Model | 2256.26171 18 125.347873 Prob > F = 0.0000
Residual | 5606.85432 1110 5.05122011 R-squared = 0.2869
-------------+------------------------------ Adj R-squared = 0.2754
Total | 7863.11603 1128 6.97084755 Root MSE = 2.2475
------------------------------------------------------------------------------
educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
meduc | .1723015 .0221964 7.76 0.000 .1287499 .2158531
feduc | .2074188 .0254604 8.15 0.000 .1574629 .2573747
age | -.2243687 .2000013 -1.12 0.262 -.616792 .1680546
agesq | .0025664 .0022605 1.14 0.256 -.001869 .0070018
black | .3667819 .2522869 1.45 0.146 -.1282311 .861795
east | .2488042 .1920135 1.30 0.195 -.1279462 .6255546
northcen | .0913945 .1757744 0.52 0.603 -.2534931 .4362821
west | .1010676 .2422408 0.42 0.677 -.3742339 .5763691
farm | -.3792615 .2143864 -1.77 0.077 -.7999099 .0413869
othrural | -.560814 .2551196 -2.20 0.028 -1.061385 -.060243
town | .0616337 .1807832 0.34 0.733 -.2930816 .416349
smcity | .0806634 .2317387 0.35 0.728 -.3740319 .5353587
y74 | .0060993 .249827 0.02 0.981 -.4840872 .4962858
y76 | .1239104 .2587922 0.48 0.632 -.3838667 .6316874
y78 | .2077861 .2627738 0.79 0.429 -.3078033 .7233755
y80 | .3828911 .2642433 1.45 0.148 -.1355816 .9013638
y82 | .5820401 .2492372 2.34 0.020 .0930108 1.071069
y84 | .4250429 .2529006 1.68 0.093 -.0711741 .92126
_cons | 13.63334 4.396773 3.10 0.002 5.006421 22.26027
------------------------------------------------------------------------------
predict v, res
reg kids educ age agesq black east northcen west farm othrural town smcity y74 y76 y78 y80 y82 y84 v
Source | SS df MS Number of obs = 1129
-------------+------------------------------ F( 18, 1110) = 9.21
Model | 400.802376 18 22.2667987 Prob > F = 0.0000
Residual | 2684.70692 1110 2.41865489 R-squared = 0.1299
-------------+------------------------------ Adj R-squared = 0.1158
Total | 3085.5093 1128 2.73538059 Root MSE = 1.5552
------------------------------------------------------------------------------
kids | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.1527395 .0392012 -3.90 0.000 -.2296562 -.0758227
age | .5235536 .1389568 3.77 0.000 .250906 .7962012
agesq | -.005716 .0015697 -3.64 0.000 -.0087959 -.0026362
black | 1.072952 .173618 6.18 0.000 .7322958 1.413609
east | .2285554 .1337787 1.71 0.088 -.0339321 .491043
northcen | .3744188 .1219925 3.07 0.002 .1350569 .6137807
west | .2076398 .1675628 1.24 0.216 -.1211357 .5364153
farm | -.0770015 .1512869 -0.51 0.611 -.373842 .2198389
othrural | -.1952451 .1814491 -1.08 0.282 -.5512671 .1607769
town | .08181 .1246122 0.66 0.512 -.162692 .3263119
smcity | .2124996 .160335 1.33 0.185 -.1020943 .5270935
y74 | .2721292 .172847 1.57 0.116 -.0670144 .6112729
y76 | -.0945483 .1791319 -0.53 0.598 -.4460236 .2569269
y78 | -.0572543 .1824512 -0.31 0.754 -.4152424 .3007337
y80 | -.053248 .1846139 -0.29 0.773 -.4154795 .3089836
y82 | -.4962149 .1764897 -2.81 0.005 -.842506 -.1499238
y84 | -.5213604 .1778207 -2.93 0.003 -.8702631 -.1724578
v | .0311374 .0443634 0.70 0.483 -.0559081 .1181829
_cons | -7.241244 3.134883 -2.31 0.021 -13.39221 -1.09028
------------------------------------------------------------------------------
Example 15.10: Job Training and Worker Productivity
use http://fmwww.bc.edu/ec-p/data/wooldridge/jtrain
tsset fcode year
sort fcode year
drop if year==1989
ivreg D.lscrap (D.hrsemp = D.grant)
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 45
-------------+------------------------------ F( 1, 43) = 3.20
Model | .274952567 1 .274952567 Prob > F = 0.0808
Residual | 17.0148863 43 .39569503 R-squared = 0.0159
-------------+------------------------------ Adj R-squared = -0.0070
Total | 17.2898389 44 .392950883 Root MSE = .62904
------------------------------------------------------------------------------
D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hrsemp |
D1 | -.0141532 .0079147 -1.79 0.081 -.0301148 .0018084
_cons | -.0326684 .1269512 -0.26 0.798 -.2886898 .223353
------------------------------------------------------------------------------
Instrumented: D.hrsemp
Instruments: D.grant
------------------------------------------------------------------------------
reg D.lscrap D.hrsemp
Source | SS df MS Number of obs = 45
-------------+------------------------------ F( 1, 43) = 2.84
Model | 1.07071319 1 1.07071319 Prob > F = 0.0993
Residual | 16.2191257 43 .377188969 R-squared = 0.0619
-------------+------------------------------ Adj R-squared = 0.0401
Total | 17.2898389 44 .392950883 Root MSE = .61416
------------------------------------------------------------------------------
D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hrsemp |
D1 | -.0076007 .0045112 -1.68 0.099 -.0166984 .0014971
_cons | -.1035161 .103736 -1.00 0.324 -.3127197 .1056875
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This page prepared by Oleksandr Talavera (revised 8 Dec 2002)
Send your questions/comments/suggestions to Kit Baum
at baum@bc.edu
These pages are maintained by the Faculty Micro
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