WOOLDRIDGE CROSS-SECTION & PANEL DATA ECONOMETRICS– EXAMPLES
CHAPTER 7 – Systems of Equations
-----------------------------------------------------------------------------------------------
name: SN
log: myReplications\iiexample7
log type: smcl
opened on: 9 Jun 2019, 18:26:33
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2016). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 15.1.
. * Chapter 7 - Estimating Systems of Equations by OLS and GLS
. * Computer Exercises (Problems)
. ******************** SETUP *********************
. // Example 7.1 NA
. // Example 7.2 NA
. // Example 7.3 (SUR System for Wages and Fringe Benefits)
. bcuse fringe, clear nodesc
. d, short
Contains data from http://fmwww.bc.edu/ec-p/data/wooldridge/fringe.dta
obs: 616
vars: 39 26 Jan 2000 12:16
size: 96,096
Sorted by:
. h sureg
. sureg (hrearn hrbens = edu exper* tenure* union south nrtheast nrthcen married white male)
Seemingly unrelated regression
--------------------------------------------------------------------------
Equation Obs Parms RMSE "R-sq" chi2 P
--------------------------------------------------------------------------
hrearn 616 12 4.3089 0.2051 158.93 0.0000
hrbens 616 12 .5152603 0.3987 408.40 0.0000
--------------------------------------------------------------------------
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hrearn |
educ | .4588139 .068393 6.71 0.000 .3247662 .5928617
exper | -.0758428 .0567371 -1.34 0.181 -.1870455 .0353598
expersq | .0039945 .0011655 3.43 0.001 .0017102 .0062787
tenure | .1100846 .0829207 1.33 0.184 -.052437 .2726062
tenuresq | -.0050706 .0032422 -1.56 0.118 -.0114252 .0012839
union | .8079933 .4034789 2.00 0.045 .0171892 1.598797
south | -.4566222 .5458508 -0.84 0.403 -1.52647 .6132258
nrtheast | -1.150759 .5993283 -1.92 0.055 -2.32542 .0239032
nrthcen | -.6362663 .5501462 -1.16 0.247 -1.714533 .4420005
married | .6423882 .4133664 1.55 0.120 -.167795 1.452571
white | 1.140891 .6054474 1.88 0.060 -.0457639 2.327546
male | 1.784702 .3937853 4.53 0.000 1.012897 2.556507
_cons | -2.632127 1.215291 -2.17 0.030 -5.014054 -.2501997
-------------+----------------------------------------------------------------
hrbens |
educ | .0767924 .0081785 9.39 0.000 .0607629 .0928219
exper | .0225649 .0067846 3.33 0.001 .0092673 .0358626
expersq | -.0004734 .0001394 -3.40 0.001 -.0007465 -.0002002
tenure | .0535556 .0099157 5.40 0.000 .0341212 .07299
tenuresq | -.0011636 .0003877 -3.00 0.003 -.0019235 -.0004038
union | .3659085 .0482482 7.58 0.000 .2713438 .4604733
south | -.0226865 .0652731 -0.35 0.728 -.1506195 .1052464
nrtheast | -.0567468 .071668 -0.79 0.428 -.1972135 .0837198
nrthcen | -.0379984 .0657867 -0.58 0.564 -.1669381 .0909413
married | .0578626 .0494306 1.17 0.242 -.0390195 .1547447
white | .0901582 .0723997 1.25 0.213 -.0517426 .232059
male | .2683383 .047089 5.70 0.000 .1760454 .3606311
_cons | -.8897471 .1453251 -6.12 0.000 -1.174579 -.6049151
------------------------------------------------------------------------------
. // Example 7.4 NA
. // Example 7.5 NA
. // Example 7.6 NA
. // Example 7.7 (Effects of Job Training Grants on Firm Scrap Rates)
. bcuse jtrain, clear nodesc
. d, short
Contains data from http://fmwww.bc.edu/ec-p/data/wooldridge/jtrain.dta
obs: 471
vars: 30 26 Jan 2000 12:16
size: 56,520
Sorted by:
. reg lscrap d88 d89 grant grant_1
Source | SS df MS Number of obs = 162
-------------+---------------------------------- F(4, 157) = 0.69
Model | 6.15830795 4 1.53957699 Prob > F = 0.5989
Residual | 349.586765 157 2.22666729 R-squared = 0.0173
-------------+---------------------------------- Adj R-squared = -0.0077
Total | 355.745073 161 2.20959673 Root MSE = 1.4922
------------------------------------------------------------------------------
lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.2393704 .3108639 -0.77 0.442 -.8533854 .3746446
d89 | -.4965237 .3379281 -1.47 0.144 -1.163996 .1709483
grant | .2000196 .3382846 0.59 0.555 -.4681564 .8681957
grant_1 | .0489357 .4360663 0.11 0.911 -.8123777 .9102492
_cons | .597434 .203063 2.94 0.004 .1963462 .9985218
------------------------------------------------------------------------------
. xtset fcode year
panel variable: fcode (strongly balanced)
time variable: year, 1987 to 1989
delta: 1 unit
. xtgls lscrap d88 d89 grant grant_1
Cross-sectional time-series FGLS regression
Coefficients: generalized least squares
Panels: homoskedastic
Correlation: no autocorrelation
Estimated covariances = 1 Number of obs = 162
Estimated autocorrelations = 0 Number of groups = 54
Estimated coefficients = 5 Time periods = 3
Wald chi2(4) = 2.85
Log likelihood = -292.1696 Prob > chi2 = 0.5826
------------------------------------------------------------------------------
lscrap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.2393704 .306029 -0.78 0.434 -.8391763 .3604354
d89 | -.4965237 .3326723 -1.49 0.136 -1.148549 .155502
grant | .2000196 .3330232 0.60 0.548 -.4526939 .8527332
grant_1 | .0489357 .4292842 0.11 0.909 -.7924458 .8903172
_cons | .597434 .1999047 2.99 0.003 .2056279 .9892401
------------------------------------------------------------------------------
. // Example 7.8 (Effect of Being in Season on Grade Point Average)
. u gpa, clear
. d, short
Contains data from gpa.dta
obs: 732
vars: 26 2 Sep 1996 22:18
size: 50,508
Sorted by: id term
. reg trmgpa spring cumgpa crsgpa frstsem season sat verbmath hsperc hssize black female
Source | SS df MS Number of obs = 732
-------------+---------------------------------- F(11, 720) = 70.64
Model | 218.156689 11 19.8324263 Prob > F = 0.0000
Residual | 202.140267 720 .280750371 R-squared = 0.5191
-------------+---------------------------------- Adj R-squared = 0.5117
Total | 420.296956 731 .574961636 Root MSE = .52986
------------------------------------------------------------------------------
trmgpa | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
spring | -.0121568 .0464813 -0.26 0.794 -.1034118 .0790983
cumgpa | .3146158 .0404916 7.77 0.000 .2351201 .3941115
crsgpa | .9840371 .0960343 10.25 0.000 .7954964 1.172578
frstsem | .7691192 .1204162 6.39 0.000 .5327104 1.005528
season | -.0462625 .0470985 -0.98 0.326 -.1387292 .0462042
sat | .0014097 .0001464 9.63 0.000 .0011223 .0016972
verbmath | -.112616 .1306157 -0.86 0.389 -.3690491 .1438171
hsperc | -.0066014 .0010195 -6.48 0.000 -.0086029 -.0045998
hssize | -.0000576 .0000994 -0.58 0.562 -.0002527 .0001375
black | -.2312855 .0543347 -4.26 0.000 -.3379589 -.1246122
female | .2855528 .0509641 5.60 0.000 .1854967 .3856089
_cons | -2.067599 .3381007 -6.12 0.000 -2.731381 -1.403818
------------------------------------------------------------------------------
. xtset id term
panel variable: id (strongly balanced)
time variable: term, 8808 to 8901, but with gaps
delta: 1 unit
. xtgls trmgpa spring cumgpa crsgpa frstsem season sat verbmath hsperc hssize black female
Cross-sectional time-series FGLS regression
Coefficients: generalized least squares
Panels: homoskedastic
Correlation: no autocorrelation
Estimated covariances = 1 Number of obs = 732
Estimated autocorrelations = 0 Number of groups = 366
Estimated coefficients = 12 Time periods = 2
Wald chi2(11) = 790.00
Log likelihood = -567.6874 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
trmgpa | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
spring | -.0121568 .0460987 -0.26 0.792 -.1025086 .078195
cumgpa | .3146158 .0401583 7.83 0.000 .2359069 .3933247
crsgpa | .9840371 .0952439 10.33 0.000 .7973625 1.170712
frstsem | .7691192 .1194251 6.44 0.000 .5350503 1.003188
season | -.0462625 .0467108 -0.99 0.322 -.137814 .045289
sat | .0014097 .0001452 9.71 0.000 .0011251 .0016943
verbmath | -.112616 .1295406 -0.87 0.385 -.366511 .1412789
hsperc | -.0066014 .0010111 -6.53 0.000 -.0085831 -.0046196
hssize | -.0000576 .0000986 -0.58 0.559 -.0002508 .0001356
black | -.2312855 .0538875 -4.29 0.000 -.336903 -.125668
female | .2855528 .0505447 5.65 0.000 .186487 .3846185
_cons | -2.067599 .3353179 -6.17 0.000 -2.724811 -1.410388
------------------------------------------------------------------------------
. // Example 7.9 (Athletes’ Grade Point Averages, continued)
. u gpa, clear
. qui reg trmgpa spring cumgpa crsgpa frstsem season sat verbmath hsperc hssize black female
. predict u, r
. by id: g u_1 = u[_n-1]
(366 missing values generated)
. reg trmgpa cumgpa crsgpa season sat verbmath hsperc hssize black female u_1
Source | SS df MS Number of obs = 366
-------------+---------------------------------- F(10, 355) = 56.83
Model | 133.915693 10 13.3915693 Prob > F = 0.0000
Residual | 83.6475687 355 .235626954 R-squared = 0.6155
-------------+---------------------------------- Adj R-squared = 0.6047
Total | 217.563261 365 .59606373 Root MSE = .48541
------------------------------------------------------------------------------
trmgpa | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cumgpa | .3488556 .0720263 4.84 0.000 .2072037 .4905075
crsgpa | 1.00062 .117679 8.50 0.000 .7691844 1.232056
season | -.0271035 .0579515 -0.47 0.640 -.141075 .086868
sat | .0014126 .0001991 7.09 0.000 .001021 .0018042
verbmath | -.1136652 .1702718 -0.67 0.505 -.4485335 .2212032
hsperc | -.0049537 .0014175 -3.49 0.001 -.0077415 -.002166
hssize | -.0000843 .0001289 -0.65 0.513 -.0003378 .0001691
black | -.2407423 .0706801 -3.41 0.001 -.3797466 -.101738
female | .291915 .0732572 3.98 0.000 .1478423 .4359876
u_1 | .1941929 .0612068 3.17 0.002 .0738194 .3145664
_cons | -2.266297 .4246611 -5.34 0.000 -3.101465 -1.431129
------------------------------------------------------------------------------
. reg u u_1
Source | SS df MS Number of obs = 366
-------------+---------------------------------- F(1, 364) = 22.04
Model | 5.08904838 1 5.08904838 Prob > F = 0.0000
Residual | 84.0489742 364 .230903775 R-squared = 0.0571
-------------+---------------------------------- Adj R-squared = 0.0545
Total | 89.1380225 365 .24421376 Root MSE = .48052
------------------------------------------------------------------------------
u | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
u_1 | .2122144 .0452035 4.69 0.000 .1233216 .3011071
_cons | 3.16e-10 .0251174 0.00 1.000 -.0493935 .0493935
------------------------------------------------------------------------------
. log close
name: SN
log: myReplications\iiexample7
log type: smcl
closed on: 9 Jun 2019, 18:26:36
-----------------------------------------------------------------------------------------------
Leave a Reply
Want to join the discussion?Feel free to contribute!