Example 13.1: Woman's Fertility Over Time
use http://fmwww.bc.edu/ec-p/data/wooldridge/fertil1
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
------------------------------------------------------------------------------
test y74 y76 y78 y80 y82 y84
( 1) y74 = 0.0
( 2) y76 = 0.0
( 3) y78 = 0.0
( 4) y80 = 0.0
( 5) y82 = 0.0
( 6) y84 = 0.0
F( 6, 1111) = 5.87
Prob > F = 0.0000
Example 13.2: Changes in the Return to Education and the Gender Wage Gap
use http://fmwww.bc.edu/ec-p/data/wooldridge/cps78_85
reg lwage y85 educ y85educ exper expersq union female y85fem
Source | SS df MS Number of obs = 1084
-------------+------------------------------ F( 8, 1075) = 99.80
Model | 135.992074 8 16.9990092 Prob > F = 0.0000
Residual | 183.099094 1075 .170324738 R-squared = 0.4262
-------------+------------------------------ Adj R-squared = 0.4219
Total | 319.091167 1083 .29463635 Root MSE = .4127
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
y85 | .1178062 .1237817 0.95 0.341 -.125075 .3606874
educ | .0747209 .0066764 11.19 0.000 .0616206 .0878212
y85educ | .0184605 .0093542 1.97 0.049 .000106 .036815
exper | .0295843 .0035673 8.29 0.000 .0225846 .036584
expersq | -.0003994 .0000775 -5.15 0.000 -.0005516 -.0002473
union | .2021319 .0302945 6.67 0.000 .1426888 .2615749
female | -.3167086 .0366215 -8.65 0.000 -.3885663 -.244851
y85fem | .085052 .051309 1.66 0.098 -.0156251 .185729
_cons | .4589329 .0934485 4.91 0.000 .2755707 .642295
------------------------------------------------------------------------------
Example 13.3: Effect of a Garbage Incinerator's Location on Housing Prices
use http://fmwww.bc.edu/ec-p/data/wooldridge/kielmc
reg rprice nearinc if year==1981
Source | SS df MS Number of obs = 142
-------------+------------------------------ F( 1, 140) = 27.73
Model | 2.7059e+10 1 2.7059e+10 Prob > F = 0.0000
Residual | 1.3661e+11 140 975815069 R-squared = 0.1653
-------------+------------------------------ Adj R-squared = 0.1594
Total | 1.6367e+11 141 1.1608e+09 Root MSE = 31238
------------------------------------------------------------------------------
rprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc | -30688.27 5827.709 -5.27 0.000 -42209.97 -19166.58
_cons | 101307.5 3093.027 32.75 0.000 95192.43 107422.6
------------------------------------------------------------------------------
scalar b1=_b[nearinc]
reg rprice nearinc if year==1978
Source | SS df MS Number of obs = 179
-------------+------------------------------ F( 1, 177) = 15.74
Model | 1.3636e+10 1 1.3636e+10 Prob > F = 0.0001
Residual | 1.5332e+11 177 866239953 R-squared = 0.0817
-------------+------------------------------ Adj R-squared = 0.0765
Total | 1.6696e+11 178 937979126 Root MSE = 29432
------------------------------------------------------------------------------
rprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc | -18824.37 4744.594 -3.97 0.000 -28187.62 -9461.118
_cons | 82517.23 2653.79 31.09 0.000 77280.09 87754.37
------------------------------------------------------------------------------
scalar b2=_b[nearinc]
The difference in two coefficients on nearinc
display b1-b2
-11863.903
reg rprice nearinc y81 y81nrinc
Source | SS df MS Number of obs = 321
-------------+------------------------------ F( 3, 317) = 22.25
Model | 6.1055e+10 3 2.0352e+10 Prob > F = 0.0000
Residual | 2.8994e+11 317 914632749 R-squared = 0.1739
-------------+------------------------------ Adj R-squared = 0.1661
Total | 3.5099e+11 320 1.0969e+09 Root MSE = 30243
------------------------------------------------------------------------------
rprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc | -18824.37 4875.322 -3.86 0.000 -28416.45 -9232.293
y81 | 18790.29 4050.065 4.64 0.000 10821.88 26758.69
y81nrinc | -11863.9 7456.646 -1.59 0.113 -26534.67 2806.866
_cons | 82517.23 2726.91 30.26 0.000 77152.1 87882.36
------------------------------------------------------------------------------
reg rprice nearinc y81 y81nrinc age agesq
Source | SS df MS Number of obs = 321
-------------+------------------------------ F( 5, 315) = 44.59
Model | 1.4547e+11 5 2.9094e+10 Prob > F = 0.0000
Residual | 2.0552e+11 315 652459465 R-squared = 0.4144
-------------+------------------------------ Adj R-squared = 0.4052
Total | 3.5099e+11 320 1.0969e+09 Root MSE = 25543
------------------------------------------------------------------------------
rprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc | 9397.936 4812.222 1.95 0.052 -70.22392 18866.1
y81 | 21321.04 3443.631 6.19 0.000 14545.62 28096.47
y81nrinc | -21920.27 6359.745 -3.45 0.001 -34433.22 -9407.322
age | -1494.424 131.8603 -11.33 0.000 -1753.862 -1234.986
agesq | 8.691277 .8481268 10.25 0.000 7.022567 10.35999
_cons | 89116.54 2406.051 37.04 0.000 84382.57 93850.5
------------------------------------------------------------------------------
reg rprice nearinc y81 y81nrinc age agesq intst land area rooms baths
Source | SS df MS Number of obs = 321
-------------+------------------------------ F( 10, 310) = 60.19
Model | 2.3167e+11 10 2.3167e+10 Prob > F = 0.0000
Residual | 1.1932e+11 310 384905873 R-squared = 0.6600
-------------+------------------------------ Adj R-squared = 0.6491
Total | 3.5099e+11 320 1.0969e+09 Root MSE = 19619
------------------------------------------------------------------------------
rprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc | 3780.334 4453.415 0.85 0.397 -4982.41 12543.08
y81 | 13928.48 2798.747 4.98 0.000 8421.533 19435.42
y81nrinc | -14177.93 4987.267 -2.84 0.005 -23991.11 -4364.759
age | -739.451 131.1272 -5.64 0.000 -997.4629 -481.4391
agesq | 3.45274 .8128214 4.25 0.000 1.853395 5.052084
intst | -.5386353 .1963359 -2.74 0.006 -.9249549 -.1523158
land | .1414196 .0310776 4.55 0.000 .0802698 .2025693
area | 18.08621 2.306064 7.84 0.000 13.54869 22.62373
rooms | 3304.225 1661.248 1.99 0.048 35.47769 6572.973
baths | 6977.318 2581.321 2.70 0.007 1898.192 12056.44
_cons | 13807.67 11166.59 1.24 0.217 -8164.23 35779.58
------------------------------------------------------------------------------
reg lprice nearinc y81 y81nrinc
Source | SS df MS Number of obs = 321
-------------+------------------------------ F( 3, 317) = 73.15
Model | 25.1331556 3 8.37771854 Prob > F = 0.0000
Residual | 36.3057473 317 .114529171 R-squared = 0.4091
-------------+------------------------------ Adj R-squared = 0.4035
Total | 61.4389029 320 .191996572 Root MSE = .33842
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc | -.3399216 .0545554 -6.23 0.000 -.4472581 -.2325851
y81 | .4569954 .0453207 10.08 0.000 .367828 .5461628
y81nrinc | -.0626505 .0834408 -0.75 0.453 -.2268181 .1015172
_cons | 11.28542 .0305144 369.84 0.000 11.22539 11.34546
------------------------------------------------------------------------------
Example 13.4: Effect of Worker Compensation laws on Duration
use http://fmwww.bc.edu/ec-p/data/wooldridge/injury
reg ldurat afchnge highearn afhigh if ky
Source | SS df MS Number of obs = 5626
-------------+------------------------------ F( 3, 5622) = 39.54
Model | 191.071427 3 63.6904757 Prob > F = 0.0000
Residual | 9055.93393 5622 1.6108029 R-squared = 0.0207
-------------+------------------------------ Adj R-squared = 0.0201
Total | 9247.00536 5625 1.64391206 Root MSE = 1.2692
------------------------------------------------------------------------------
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
afhigh | .1906012 .0685089 2.78 0.005 .0562973 .3249051
_cons | 1.125615 .0307368 36.62 0.000 1.065359 1.185871
------------------------------------------------------------------------------
Example 13.5: Sleeping Versus Working
use http://fmwww.bc.edu/ec-p/data/wooldridge/slp75_81
reg cslpnap ctotwrk ceduc cmarr cyngkid cgdhlth
Source | SS df MS Number of obs = 239
-------------+------------------------------ F( 5, 233) = 8.19
Model | 14674698.2 5 2934939.64 Prob > F = 0.0000
Residual | 83482611.7 233 358294.471 R-squared = 0.1495
-------------+------------------------------ Adj R-squared = 0.1313
Total | 98157309.9 238 412425.672 Root MSE = 598.58
------------------------------------------------------------------------------
cslpnap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ctotwrk | -.2266694 .036054 -6.29 0.000 -.2977029 -.1556359
ceduc | -.0244717 48.75938 -0.00 1.000 -96.09007 96.04113
cmarr | 104.2139 92.85536 1.12 0.263 -78.72946 287.1574
cyngkid | 94.6654 87.65252 1.08 0.281 -78.02738 267.3582
cgdhlth | 87.57785 76.59913 1.14 0.254 -63.33758 238.4933
_cons | -92.63404 45.8659 -2.02 0.045 -182.9989 -2.269154
------------------------------------------------------------------------------
test ceduc cmarr cyngkid cgdhlth
( 1) ceduc = 0.0
( 2) cmarr = 0.0
( 3) cyngkid = 0.0
( 4) cgdhlth = 0.0
F( 4, 233) = 0.86
Prob > F = 0.4857
Example 13.6: Distributed Lag of Crime Rate on Clear-up Rate
use http://fmwww.bc.edu/ec-p/data/wooldridge/crime3
reg clcrime cclrprc1 cclrprc2
Source | SS df MS Number of obs = 53
-------------+------------------------------ F( 2, 50) = 5.99
Model | 1.42294706 2 .711473529 Prob > F = 0.0046
Residual | 5.93723982 50 .118744796 R-squared = 0.1933
-------------+------------------------------ Adj R-squared = 0.1611
Total | 7.36018687 52 .141542055 Root MSE = .34459
------------------------------------------------------------------------------
clcrime | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cclrprc1 | -.0040475 .0047199 -0.86 0.395 -.0135276 .0054326
cclrprc2 | -.0131966 .0051946 -2.54 0.014 -.0236302 -.0027629
_cons | .0856556 .0637825 1.34 0.185 -.0424553 .2137665
------------------------------------------------------------------------------
Example 13.7: Effect of Drunk Driving Laws on Traffic Fatalities
use http://fmwww.bc.edu/ec-p/data/wooldridge/traffic1
reg cdthrte copen cadmn
Source | SS df MS Number of obs = 51
-------------+------------------------------ F( 2, 48) = 3.23
Model | .762579679 2 .38128984 Prob > F = 0.0482
Residual | 5.6636945 48 .117993635 R-squared = 0.1187
-------------+------------------------------ Adj R-squared = 0.0819
Total | 6.42627418 50 .128525484 Root MSE = .3435
------------------------------------------------------------------------------
cdthrte | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
copen | -.4196787 .2055948 -2.04 0.047 -.8330547 -.0063028
cadmn | -.1506024 .1168223 -1.29 0.204 -.3854894 .0842846
_cons | -.4967872 .0524256 -9.48 0.000 -.6021959 -.3913784
------------------------------------------------------------------------------
Example 13.8: Effect of Enterprise Zones on Unemployment Claims
use http://fmwww.bc.edu/ec-p/data/wooldridge/ezunem
reg guclms d82 d83 d84 d85 d86 d87 d88 cez
Source | SS df MS Number of obs = 176
-------------+------------------------------ F( 8, 167) = 34.50
Model | 12.8826331 8 1.61032914 Prob > F = 0.0000
Residual | 7.79583789 167 .046681664 R-squared = 0.6230
-------------+------------------------------ Adj R-squared = 0.6049
Total | 20.678471 175 .118162691 Root MSE = .21606
------------------------------------------------------------------------------
guclms | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d82 | .7787595 .0651444 11.95 0.000 .6501469 .9073721
d83 | -.0331192 .0651444 -0.51 0.612 -.1617318 .0954934
d84 | -.0171382 .0685455 -0.25 0.803 -.1524655 .118189
d85 | .323081 .0666774 4.85 0.000 .1914418 .4547202
d86 | .292154 .0651444 4.48 0.000 .1635413 .4207666
d87 | .0539481 .0651444 0.83 0.409 -.0746645 .1825607
d88 | -.0170526 .0651444 -0.26 0.794 -.1456652 .11156
cez | -.1818775 .0781862 -2.33 0.021 -.3362382 -.0275169
_cons | -.3216319 .046064 -6.98 0.000 -.4125748 -.230689
------------------------------------------------------------------------------
bpagan d82 d83 d84 d85 d86 d87 d88 cez
Breusch-Pagan LM statistic: 6.58428 Chi-sq( 8) P-value = .5821
Example 13.9: Country Crime Rates in North Carolina
use http://fmwww.bc.edu/ec-p/data/wooldridge/crime4
reg clcrmrte d83 d84 d85 d86 d87 clprbarr clprbcon clprbpri clavgsen clpolpc
Source | SS df MS Number of obs = 540
-------------+------------------------------ F( 10, 529) = 40.32
Model | 9.60042816 10 .960042816 Prob > F = 0.0000
Residual | 12.5963761 529 .023811675 R-squared = 0.4325
-------------+------------------------------ Adj R-squared = 0.4218
Total | 22.1968043 539 .041181455 Root MSE = .15431
------------------------------------------------------------------------------
clcrmrte | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d83 | -.0998658 .0238953 -4.18 0.000 -.1468071 -.0529246
d84 | -.0479374 .0235021 -2.04 0.042 -.0941063 -.0017686
d85 | -.0046111 .0234998 -0.20 0.845 -.0507756 .0415533
d86 | .0275143 .0241494 1.14 0.255 -.0199261 .0749548
d87 | .0408267 .0244153 1.67 0.095 -.0071361 .0887895
clprbarr | -.3274942 .0299801 -10.92 0.000 -.3863889 -.2685994
clprbcon | -.2381066 .0182341 -13.06 0.000 -.2739268 -.2022864
clprbpri | -.1650462 .025969 -6.36 0.000 -.2160613 -.1140312
clavgsen | -.0217607 .0220909 -0.99 0.325 -.0651574 .0216361
clpolpc | .3984264 .026882 14.82 0.000 .3456177 .4512351
_cons | .0077134 .0170579 0.45 0.651 -.0257961 .0412229
------------------------------------------------------------------------------
whitetst, fitted
White's special test statistic : 118.4921 Chi-sq( 2) P-value = 1.9e-26
This page prepared by Oleksandr Talavera (revised 8 Nov 2002)
Send your questions/comments/suggestions to Kit Baum
at baum@bc.edu
These pages are maintained by the Faculty Micro
Resource Center's GSA Program,
a unit of Boston College Academic Technology Services