Chapter 6 - Additional Single-Equation Topics
Examples
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name: SN
log: myReplications\iiexample6
log type: smcl
opened on: 6 Jun 2019, 00:53:26
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2010). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 15.1.
. * Chapter 6 - Additional Single-Equation Topics
. * Computer Exercises (Problems)
. * Nov 21, 2018
. **********************************************
. //Example 6.1 Testing for endogenity of educ in wage equation
. bcuse mroz, clear nodesc
. reg educ exper* mothed fathed husedu
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(5, 747) = 130.16
Model | 1820.49038 5 364.098077 Prob > F = 0.0000
Residual | 2089.54946 747 2.79725496 R-squared = 0.4656
-------------+---------------------------------- Adj R-squared = 0.4620
Total | 3910.03984 752 5.19952106 Root MSE = 1.6725
------------------------------------------------------------------------------
educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0532406 .0218443 2.44 0.015 .0103571 .0961241
expersq | -.0007403 .000708 -1.05 0.296 -.0021303 .0006497
motheduc | .130004 .0223789 5.81 0.000 .086071 .1739371
fatheduc | .1013613 .0214423 4.73 0.000 .059267 .1434556
huseduc | .3715645 .0220465 16.85 0.000 .3282839 .414845
_cons | 5.115778 .298017 17.17 0.000 4.530727 5.700828
------------------------------------------------------------------------------
. predict v2, residual
. reg lwage exper* educ v2
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(4, 423) = 20.52
Model | 36.285365 4 9.07134126 Prob > F = 0.0000
Residual | 187.042086 423 .442179873 R-squared = 0.1625
-------------+---------------------------------- Adj R-squared = 0.1546
Total | 223.327451 427 .523015108 Root MSE = .66497
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0438739 .0132172 3.32 0.001 .0178945 .0698534
expersq | -.0008708 .000394 -2.21 0.028 -.0016452 -.0000964
educ | .0801296 .0214782 3.73 0.000 .0379123 .122347
v2 | .0478873 .028334 1.69 0.092 -.0078056 .1035803
_cons | -.2008037 .2746073 -0.73 0.465 -.7405684 .3389611
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. //Example 6.2
. bcuse card, clear nodesc
. gen b_edu=black*edu
. reg lwage educ black b_edu exper* smsa smsa66 south reg661-reg668
Source | SS df MS Number of obs = 3,010
-------------+---------------------------------- F(16, 2993) = 80.83
Model | 178.817017 16 11.1760636 Prob > F = 0.0000
Residual | 413.824594 2,993 .138264148 R-squared = 0.3017
-------------+---------------------------------- Adj R-squared = 0.2980
Total | 592.641611 3,009 .196956335 Root MSE = .37184
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0707788 .0037548 18.85 0.000 .0634165 .0781411
black | -.4191076 .0794021 -5.28 0.000 -.5747958 -.2634194
b_edu | .0178595 .006271 2.85 0.004 .0055636 .0301554
exper | .0821556 .0066828 12.29 0.000 .0690522 .0952589
expersq | -.0021349 .0003207 -6.66 0.000 -.0027638 -.001506
smsa | .1340695 .0200931 6.67 0.000 .0946718 .1734671
smsa66 | .0249824 .0194297 1.29 0.199 -.0131144 .0630793
south | -.1441927 .0259827 -5.55 0.000 -.1951384 -.093247
reg661 | -.1221745 .0388047 -3.15 0.002 -.1982611 -.046088
reg662 | -.0232881 .0282266 -0.83 0.409 -.0786336 .0320574
reg663 | .0230953 .0273506 0.84 0.399 -.0305325 .0767231
reg664 | -.0666851 .0356556 -1.87 0.062 -.1365971 .0032269
reg665 | .0032644 .03614 0.09 0.928 -.0675974 .0741261
reg666 | .0151249 .0401224 0.38 0.706 -.0635454 .0937952
reg667 | -.0074966 .0394073 -0.19 0.849 -.0847648 .0697716
reg668 | -.1757195 .0462851 -3.80 0.000 -.2664733 -.0849657
_cons | 4.80677 .0752604 63.87 0.000 4.659202 4.954337
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. //
. gen b_nearc4=black*nearc4
. qui reg educ black exper* smsa smsa66 south reg661-reg668 nearc4 b_nearc4
. predict v21, residual
. qui reg b_edu black exper* smsa smsa66 south reg661-reg668 nearc4 b_nearc4
. predict v22, residual
. reg lwage educ b_edu black exper* smsa smsa66 south reg661-reg668 v2*
Source | SS df MS Number of obs = 3,010
-------------+---------------------------------- F(18, 2991) = 71.89
Model | 178.967071 18 9.94261507 Prob > F = 0.0000
Residual | 413.67454 2,991 .138306433 R-squared = 0.3020
-------------+---------------------------------- Adj R-squared = 0.2978
Total | 592.641611 3,009 .196956335 Root MSE = .3719
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lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .1273556 .0547317 2.33 0.020 .02004 .2346712
b_edu | .0109036 .0387795 0.28 0.779 -.0651337 .0869408
black | -.282765 .4866263 -0.58 0.561 -1.236921 .6713912
exper | .1059116 .0241963 4.38 0.000 .0584685 .1533547
expersq | -.0022406 .0004635 -4.83 0.000 -.0031493 -.0013318
smsa | .1111556 .0304028 3.66 0.000 .0515431 .1707681
smsa66 | .0180009 .0207769 0.87 0.386 -.0227375 .0587393
south | -.1424762 .0272675 -5.23 0.000 -.1959412 -.0890112
reg661 | -.1103479 .0410557 -2.69 0.007 -.1908481 -.0298477
reg662 | -.0081783 .0317789 -0.26 0.797 -.070489 .0541325
reg663 | .0382414 .0314436 1.22 0.224 -.0234119 .0998946
reg664 | -.0600379 .0368007 -1.63 0.103 -.1321951 .0121194
reg665 | .0337805 .0479745 0.70 0.481 -.060286 .1278469
reg666 | .0498975 .0537534 0.93 0.353 -.0554998 .1552948
reg667 | .0216942 .0501526 0.43 0.665 -.0766428 .1200312
reg668 | -.1908353 .0485659 -3.93 0.000 -.2860613 -.0956092
v21 | -.0568274 .0548612 -1.04 0.300 -.1643969 .0507422
v22 | .0070106 .0392971 0.18 0.858 -.0700415 .0840627
_cons | 3.84499 .9314527 4.13 0.000 2.018638 5.671343
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. test v21 v22
( 1) v21 = 0
( 2) v22 = 0
F( 2, 2991) = 0.54
Prob > F = 0.5814
. ivreg lwage (educ b_edu = nearc4 b_nearc4) black exper* smsa smsa66 south reg661-reg668
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 3,010
-------------+---------------------------------- F(16, 2993) = 48.15
Model | 144.325574 16 9.02034836 Prob > F = 0.0000
Residual | 448.316037 2,993 .149788185 R-squared = 0.2435
-------------+---------------------------------- Adj R-squared = 0.2395
Total | 592.641611 3,009 .196956335 Root MSE = .38702
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lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .1273556 .0569582 2.24 0.025 .0156743 .2390369
b_edu | .0109036 .0403571 0.27 0.787 -.0682269 .0900341
black | -.282765 .5064227 -0.56 0.577 -1.275737 .7102069
exper | .1059116 .0251806 4.21 0.000 .0565385 .1552847
expersq | -.0022406 .0004823 -4.65 0.000 -.0031863 -.0012949
smsa | .1111556 .0316396 3.51 0.000 .049118 .1731932
smsa66 | .0180009 .0216221 0.83 0.405 -.0243947 .0603966
south | -.1424762 .0283768 -5.02 0.000 -.1981161 -.0868362
reg661 | -.1103479 .0427258 -2.58 0.010 -.1941229 -.0265729
reg662 | -.0081783 .0330717 -0.25 0.805 -.0730239 .0566673
reg663 | .0382414 .0327227 1.17 0.243 -.02592 .1024027
reg664 | -.0600379 .0382978 -1.57 0.117 -.1351305 .0150548
reg665 | .0337805 .0499262 0.68 0.499 -.0641127 .1316736
reg666 | .0498975 .0559401 0.89 0.372 -.0597874 .1595825
reg667 | .0216942 .0521928 0.42 0.678 -.0806432 .1240317
reg668 | -.1908353 .0505417 -3.78 0.000 -.2899352 -.0917354
_cons | 3.844991 .969345 3.97 0.000 1.944341 5.745641
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Instrumented: educ b_edu
Instruments: black exper expersq smsa smsa66 south reg661 reg662 reg663
reg664 reg665 reg666 reg667 reg668 nearc4 b_nearc4
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. //Example 6.3 Overidentifying restriction in the wage equation
. bcuse mroz, clear nodesc
. ivreg lwage exper* (educ=mothed fathed hused)
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
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Instrumented: educ
Instruments: exper expersq motheduc fatheduc huseduc
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. predict u, residual
(325 missing values generated)
. reg u exper* mothed fathed hused
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(5, 422) = 0.22
Model | .494825844 5 .098965169 Prob > F = 0.9537
Residual | 189.439884 422 .448909678 R-squared = 0.0026
-------------+---------------------------------- Adj R-squared = -0.0092
Total | 189.93471 427 .444811967 Root MSE = .67001
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u | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .000056 .0132285 0.00 0.997 -.025946 .026058
expersq | -8.88e-06 .0003956 -0.02 0.982 -.0007865 .0007687
motheduc | -.0103852 .0118688 -0.87 0.382 -.0337145 .0129442
fatheduc | .0006734 .0113798 0.06 0.953 -.0216948 .0230417
huseduc | .0067811 .0114259 0.59 0.553 -.0156776 .0292398
_cons | .0086063 .1772724 0.05 0.961 -.3398405 .3570532
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. display "LM = " e(N)* e(r2) " & p = " chi2tail(2, e(r2)*e(N))
LM = 1.1150435 & p = .57262642
. //Hetroskedasticity Robust
. ivreg lwage exper* (educ=mothed fathed hused), r
Instrumental variables (2SLS) regression Number of obs = 428
F(3, 424) = 9.19
Prob > F = 0.0000
R-squared = 0.1495
Root MSE = .6693
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| Robust
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0803918 .0217033 3.70 0.000 .0377323 .1230512
exper | .0430973 .0153064 2.82 0.005 .0130114 .0731832
expersq | -.0008628 .0004217 -2.05 0.041 -.0016916 -.000034
_cons | -.1868574 .3012625 -0.62 0.535 -.7790113 .4052966
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Instrumented: educ
Instruments: exper expersq motheduc fatheduc huseduc
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. //Hetroskedasticity using LM statistic page137
. reg edu exper* mothed fathed hused
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(5, 747) = 130.16
Model | 1820.49038 5 364.098077 Prob > F = 0.0000
Residual | 2089.54946 747 2.79725496 R-squared = 0.4656
-------------+---------------------------------- Adj R-squared = 0.4620
Total | 3910.03984 752 5.19952106 Root MSE = 1.6725
------------------------------------------------------------------------------
educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0532406 .0218443 2.44 0.015 .0103571 .0961241
expersq | -.0007403 .000708 -1.05 0.296 -.0021303 .0006497
motheduc | .130004 .0223789 5.81 0.000 .086071 .1739371
fatheduc | .1013613 .0214423 4.73 0.000 .059267 .1434556
huseduc | .3715645 .0220465 16.85 0.000 .3282839 .414845
_cons | 5.115778 .298017 17.17 0.000 4.530727 5.700828
------------------------------------------------------------------------------
. predict eduhat, xb
. reg mothed exper* eduhat
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(3, 749) = 187.97
Model | 3662.73307 3 1220.91102 Prob > F = 0.0000
Residual | 4864.82868 749 6.49509837 R-squared = 0.4295
-------------+---------------------------------- Adj R-squared = 0.4272
Total | 8527.56175 752 11.3398428 Root MSE = 2.5485
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motheduc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.1051582 .0337061 -3.12 0.002 -.1713279 -.0389885
expersq | .0015231 .0010851 1.40 0.161 -.000607 .0036532
eduhat | 1.425138 .0608066 23.44 0.000 1.305767 1.54451
_cons | -7.412718 .7419038 -9.99 0.000 -8.869176 -5.95626
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. predict r1, residual
. reg fathed exper* eduhat
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(3, 749) = 197.80
Model | 4242.00753 3 1414.00251 Prob > F = 0.0000
Residual | 5354.45462 749 7.14880456 R-squared = 0.4420
-------------+---------------------------------- Adj R-squared = 0.4398
Total | 9596.46215 752 12.7612529 Root MSE = 2.6737
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fatheduc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.1068543 .0353617 -3.02 0.003 -.1762741 -.0374346
expersq | .0014908 .0011384 1.31 0.191 -.0007439 .0037256
eduhat | 1.534126 .0637932 24.05 0.000 1.408891 1.65936
_cons | -9.170285 .7783437 -11.78 0.000 -10.69828 -7.64229
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. predict r2, residual
. gen one=1
. reg one c.u#c.r1 c.u#c.r2, noc
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(2, 426) = 0.51
Model | 1.01874532 2 .509372658 Prob > F = 0.6019
Residual | 426.981255 426 1.00230341 R-squared = 0.0024
-------------+---------------------------------- Adj R-squared = -0.0023
Total | 428 428 1 Root MSE = 1.0012
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one | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
c.u#c.r1 | -.0270098 .028959 -0.93 0.352 -.0839302 .0299106
|
c.u#c.r2 | -.0004977 .0307894 -0.02 0.987 -.0610157 .0600203
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. display "LM = " e(N) - e(rss) " & p = " chi2tail(2, e(r2)*e(N))
LM = 1.0187453 & p = .60087241
. //Example 6.4 Testing for neglected nonlinearities in a wage equation
. bcuse nls80, clear nodesc
. reg lwage exper tenure married south urban black educ
Source | SS df MS Number of obs = 935
-------------+---------------------------------- F(7, 927) = 44.75
Model | 41.8377619 7 5.97682312 Prob > F = 0.0000
Residual | 123.818521 927 .133569063 R-squared = 0.2526
-------------+---------------------------------- Adj R-squared = 0.2469
Total | 165.656283 934 .177362188 Root MSE = .36547
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lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .014043 .0031852 4.41 0.000 .007792 .020294
tenure | .0117473 .002453 4.79 0.000 .0069333 .0165613
married | .1994171 .0390502 5.11 0.000 .1227801 .276054
south | -.0909036 .0262485 -3.46 0.001 -.142417 -.0393903
urban | .1839121 .0269583 6.82 0.000 .1310056 .2368185
black | -.1883499 .0376666 -5.00 0.000 -.2622717 -.1144281
educ | .0654307 .0062504 10.47 0.000 .0531642 .0776973
_cons | 5.395497 .113225 47.65 0.000 5.17329 5.617704
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. predict u, residual
. predict lwageh, xb
. gen lwage2 = lwageh^2
. gen lwage3 = lwageh^3
. reg u exper tenure married south urban black educ lwage2 lwage3
Source | SS df MS Number of obs = 935
-------------+---------------------------------- F(9, 925) = 0.04
Model | .043635869 9 .00484843 Prob > F = 1.0000
Residual | 123.774885 925 .133810687 R-squared = 0.0004
-------------+---------------------------------- Adj R-squared = -0.0094
Total | 123.818521 934 .13256801 Root MSE = .3658
------------------------------------------------------------------------------
u | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.7632267 1.397385 -0.55 0.585 -3.50564 1.979186
tenure | -.6384821 1.169016 -0.55 0.585 -2.932713 1.655748
married | -10.83869 19.8426 -0.55 0.585 -49.78043 28.10305
south | 4.941136 9.046332 0.55 0.585 -12.81258 22.69485
urban | -9.997095 18.30255 -0.55 0.585 -45.91644 25.92225
black | 10.23873 18.74209 0.55 0.585 -26.54323 47.02068
educ | -3.555951 6.510939 -0.55 0.585 -16.33388 9.221974
lwage2 | 8.092523 14.74836 0.55 0.583 -20.85161 37.03666
lwage3 | -.4012701 .7281559 -0.55 0.582 -1.830299 1.027759
_cons | -171.6899 313.2923 -0.55 0.584 -786.5359 443.1562
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. display "LM = " e(N)* e(r2) " & p = " chi2tail(2, e(r2)*e(N))
LM = .32951078 & p = .84810113
. //Example 6.5 Length of Time on Workers Compensation
. bcuse injury, clear nodesc
. reg ldurat afchnge highearn c.afchnge#c.highearn if ky==1
Source | SS df MS Number of obs = 5,626
-------------+---------------------------------- F(3, 5622) = 39.54
Model | 191.071427 3 63.6904757 Prob > F = 0.0000
Residual | 9055.93393 5,622 1.6108029 R-squared = 0.0207
-------------+---------------------------------- Adj R-squared = 0.0201
Total | 9247.00536 5,625 1.64391206 Root MSE = 1.2692
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ldurat | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------------+----------------------------------------------------------------
afchnge | .0076573 .0447173 0.17 0.864 -.0800058 .0953204
highearn | .2564785 .0474464 5.41 0.000 .1634652 .3494918
|
c.afchnge#c.highearn | .1906012 .0685089 2.78 0.005 .0562973 .3249051
|
_cons | 1.125615 .0307368 36.62 0.000 1.065359 1.185871
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. log close
name: SN
log: myReplications\iiexample6
log type: smcl
closed on: 6 Jun 2019, 00:53:31
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