WOOLDRIDGE CROSS-SECTION & PANEL DATA ECONOMETRICS– EXAMPLES
CHAPTER 5 – Instrumental Variables Estimation of Single-Equation Linear Models
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name: SN
log: myReplications\iiexample5
log type: smcl
opened on: 5 Jun 2019, 22:10:25
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2016). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 15.1.
. * Chapter 5 - IV estimation of Single-Equation Linear Models
. * Computer Exercises (Problems)
. ******************** SETUP *********************
. //Chap5 Examples - IV Estimation of Single-Equation Linear Models
. //Example 5.1 NA
. //Example 5.2 NA
. //Example 5.3 Parents’ and Husband’s Education as IVs
. bcuse mroz, clear nodesc
. reg lwage exper expersq educ
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
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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 | -.5220407 .1986321 -2.63 0.009 -.9124668 -.1316145
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. ivreg lwage exper expersq (educ=mothedu fatheduc huseduc)
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
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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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. test educ
( 1) educ = 0
F( 1, 424) = 13.63
Prob > F = 0.0003
. //Example 5.4 Parents’ and Husband’s Education as IVs, cont'd
. //F-test
. bcuse mroz, clear nodesc
. ivreg lwage exper expersq (educ=mothedu fatheduc huseduc) kidslt6 kidsge6
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(5, 422) = 7.08
Model | 33.6045899 5 6.72091798 Prob > F = 0.0000
Residual | 189.722861 422 .44958024 R-squared = 0.1505
-------------+---------------------------------- Adj R-squared = 0.1404
Total | 223.327451 427 .523015108 Root MSE = .67051
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lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0798678 .0223575 3.57 0.000 .035922 .1238137
exper | .0414939 .0134453 3.09 0.002 .0150658 .0679221
expersq | -.0008576 .0003972 -2.16 0.031 -.0016384 -.0000768
kidslt6 | -.0313333 .0861049 -0.36 0.716 -.2005811 .1379146
kidsge6 | -.0182225 .0271427 -0.67 0.502 -.0715741 .0351292
_cons | -.1315326 .3038534 -0.43 0.665 -.7287873 .4657221
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Instrumented: educ
Instruments: exper expersq kidslt6 kidsge6 motheduc fatheduc huseduc
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. test kidslt6 kidsge6
( 1) kidslt6 = 0
( 2) kidsge6 = 0
F( 2, 422) = 0.31
Prob > F = 0.7368
. //LM test
. qui ivreg lwage exper expersq (educ=mothedu fatheduc huseduc)
. predict u2, residuals
(325 missing values generated)
. qui ivreg u2 exper expersq (educ=mothedu fatheduc huseduc) kidslt6 kidsge6
. display "LM = " e(N)* e(r2) " & p = " chi2tail(2, e(r2)*e(N))
LM = .47737781 & p = .78765988
. // Hetroskedasticity-Robust Inference pp106
. ivreg lwage exper expersq (educ=mothedu fatheduc huseduc), 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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. //Hteroskedasticity robust F test, page 107 (F=.25 & p-value=.781)
. ivreg lwage exper* (edu=mothedu fathed hused) kids*, r
Instrumental variables (2SLS) regression Number of obs = 428
F(5, 422) = 5.96
Prob > F = 0.0000
R-squared = 0.1505
Root MSE = .67051
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| Robust
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0798678 .0224613 3.56 0.000 .0357179 .1240177
exper | .0414939 .0154463 2.69 0.008 .0111328 .0718551
expersq | -.0008576 .0004212 -2.04 0.042 -.0016856 -.0000297
kidslt6 | -.0313333 .1013967 -0.31 0.757 -.2306389 .1679723
kidsge6 | -.0182225 .0283579 -0.64 0.521 -.0739629 .0375179
_cons | -.1315326 .320634 -0.41 0.682 -.7617712 .498706
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Instrumented: educ
Instruments: exper expersq kidslt6 kidsge6 motheduc fatheduc huseduc
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. test kidslt kidsg
( 1) kidslt6 = 0
( 2) kidsge6 = 0
F( 2, 422) = 0.25
Prob > F = 0.7812
. //LM test page 107
. ivreg lwage exper* (edu=mothedu 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
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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)
. foreach x of var kidslt kidsg{
2. ivreg `x' exper* (edu=mothedu fathed hused)
3. predict r_`x', residual
4. gen ures`x'= u*r_`x'
5. }
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(3, 749) = 14.83
Model | 9.46507581 3 3.15502527 Prob > F = 0.0000
Residual | 196.983795 749 .262995721 R-squared = 0.0458
-------------+---------------------------------- Adj R-squared = 0.0420
Total | 206.448871 752 .274533073 Root MSE = .51283
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kidslt6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0472331 .0122358 3.86 0.000 .0232126 .0712536
exper | -.0164951 .0067825 -2.43 0.015 -.0298101 -.0031801
expersq | .0001038 .0002183 0.48 0.635 -.0003248 .0005324
_cons | -.1857562 .1492894 -1.24 0.214 -.4788317 .1073193
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Instrumented: educ
Instruments: exper expersq motheduc fatheduc huseduc
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(325 missing values generated)
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(3, 749) = 25.64
Model | 123.638191 3 41.2127304 Prob > F = 0.0000
Residual | 1186.39634 749 1.58397375 R-squared = 0.0944
-------------+---------------------------------- Adj R-squared = 0.0908
Total | 1310.03453 752 1.74206719 Root MSE = 1.2586
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kidsge6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.0150236 .0300284 -0.50 0.617 -.0739734 .0439262
exper | -.0233173 .0166452 -1.40 0.162 -.0559942 .0093595
expersq | -.0008754 .0005358 -1.63 0.103 -.0019273 .0001765
_cons | 1.941585 .3663774 5.30 0.000 1.222336 2.660834
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Instrumented: educ
Instruments: exper expersq motheduc fatheduc huseduc
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(325 missing values generated)
. gen one=1
. reg one ures*, noc
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(2, 426) = 0.43
Model | .867685628 2 .433842814 Prob > F = 0.6490
Residual | 427.132314 426 1.00265801 R-squared = 0.0020
-------------+---------------------------------- Adj R-squared = -0.0027
Total | 428 428 1 Root MSE = 1.0013
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one | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ureskidslt6 | -.1150184 .1606478 -0.72 0.474 -.4307793 .2007425
ureskidsge6 | -.0284026 .056554 -0.50 0.616 -.1395622 .0827569
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. display "LM= N-SSRo = " e(N)-e(rss) " & p = " chi2tail(3, e(N)-e(rss))
LM= N-SSRo = .86768563 & p = .83321816
. //Example 5.5 IQ and KWW as indicator of Ability
. bcuse nls80, clear nodesc
. ivreg lwage exper tenur marri south urban black edu (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
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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
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Instrumented: iq
Instruments: exper tenure married south urban black educ kww
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. log close
name: SN
log: myReplications\iiexample5
log type: smcl
closed on: 5 Jun 2019, 22:10:27
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