Example 4.1: Hourly Wage Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/wage1
reg lwage educ exper tenure
Source | SS df MS Number of obs = 526
---------+------------------------------ F( 3, 522) = 80.39
Model | 46.8741805 3 15.6247268 Prob > F = 0.0000
Residual | 101.455581 522 .194359351 R-squared = 0.3160
---------+------------------------------ Adj R-squared = 0.3121
Total | 148.329762 525 .28253288 Root MSE = .44086
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
educ | .092029 .0073299 12.555 0.000 .0776292 .1064288
exper | .0041211 .0017233 2.391 0.017 .0007357 .0075065
tenure | .0220672 .0030936 7.133 0.000 .0159897 .0281448
_cons | .2843595 .1041904 2.729 0.007 .0796755 .4890435
------------------------------------------------------------------------------
Inclease in log(wage) if experience increases by 3 years
display _b[exper]*3
.0123
Example 4.2: Student Performance and School Size
use http://fmwww.bc.edu/ec-p/data/wooldridge/meap93
reg math10 totcomp staff enroll
Source | SS df MS Number of obs = 408
---------+------------------------------ F( 3, 404) = 7.70
Model | 2422.93434 3 807.644779 Prob > F = 0.0001
Residual | 42394.2462 404 104.936253 R-squared = 0.0541
---------+------------------------------ Adj R-squared = 0.0470
Total | 44817.1805 407 110.115923 Root MSE = 10.244
------------------------------------------------------------------------------
math10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
totcomp | .0004586 .0001004 4.570 0.000 .0002613 .0006559
staff | .0479199 .039814 1.204 0.229 -.0303487 .1261884
enroll | -.0001976 .0002152 -0.918 0.359 -.0006207 .0002255
_cons | 2.274021 6.113794 0.372 0.710 -9.7448 14.29284
------------------------------------------------------------------------------
reg math10 ltotcomp lstaff lenroll
Source | SS df MS Number of obs = 408
---------+------------------------------ F( 3, 404) = 9.42
Model | 2930.03231 3 976.677437 Prob > F = 0.0000
Residual | 41887.1482 404 103.68106 R-squared = 0.0654
---------+------------------------------ Adj R-squared = 0.0584
Total | 44817.1805 407 110.115923 Root MSE = 10.182
------------------------------------------------------------------------------
math10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
ltotcomp | 21.15498 4.055549 5.216 0.000 13.18237 29.1276
lstaff | 3.979981 4.189659 0.950 0.343 -4.256274 12.21624
lenroll | -1.268042 .6932037 -1.829 0.068 -2.630778 .094695
_cons | -207.6645 48.70311 -4.264 0.000 -303.4077 -111.9213
------------------------------------------------------------------------------
Change in math10 if enrollment increases by 1 percent
display _b[lenrol]/100
-.013
Example 4.3: Determinants of College GPA
use http://fmwww.bc.edu/ec-p/data/wooldridge/gpa1
reg colGPA hsGPA ACT skipped
Source | SS df MS Number of obs = 141
---------+------------------------------ F( 3, 137) = 13.92
Model | 4.53313314 3 1.51104438 Prob > F = 0.0000
Residual | 14.8729663 137 .108561798 R-squared = 0.2336
---------+------------------------------ Adj R-squared = 0.2168
Total | 19.4060994 140 .138614996 Root MSE = .32949
------------------------------------------------------------------------------
colGPA | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
hsGPA | .4118162 .0936742 4.396 0.000 .2265819 .5970505
ACT | .0147202 .0105649 1.393 0.166 -.0061711 .0356115
skipped | -.0831131 .0259985 -3.197 0.002 -.1345234 -.0317028
_cons | 1.389554 .3315535 4.191 0.000 .7339295 2.045178
------------------------------------------------------------------------------
Example 4.4: Campus Crime and Enrollment
use http://fmwww.bc.edu/ec-p/data/wooldridge/campus
reg lcrime lenroll
Source | SS df MS Number of obs = 97
-------------+------------------------------ F( 1, 95) = 133.79
Model | 107.083654 1 107.083654 Prob > F = 0.0000
Residual | 76.0358244 95 .800377098 R-squared = 0.5848
-------------+------------------------------ Adj R-squared = 0.5804
Total | 183.119479 96 1.90749457 Root MSE = .89464
------------------------------------------------------------------------------
lcrime | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lenroll | 1.26976 .109776 11.57 0.000 1.051827 1.487693
_cons | -6.63137 1.03354 -6.42 0.000 -8.683206 -4.579533
------------------------------------------------------------------------------
T-statistics for testing the coefficient on lenrol equal to 1
scalar tvalue=(_b[lenrol]-1)/_se[lenrol]
scalar pvalue=ttail(120,tvalue)
display "T-value: " tvalue ", P=value: " pvalue
T-statistics: 2.45737, P=value: .00771259
test lenroll=1
( 1) lenroll = 1.0
F( 1, 95) = 6.04
Prob > F = 0.0158
Example 4.5: Housing Prices and Air Pollution
use http://fmwww.bc.edu/ec-p/data/wooldridge/hprice2
gen ldist=log(dist)
reg lprice lnox ldist rooms stratio
Source | SS df MS Number of obs = 506
---------+------------------------------ F( 4, 501) = 175.86
Model | 49.3987735 4 12.3496934 Prob > F = 0.0000
Residual | 35.1834974 501 .070226542 R-squared = 0.5840
---------+------------------------------ Adj R-squared = 0.5807
Total | 84.5822709 505 .167489645 Root MSE = .265
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lnox | -.95354 .1167418 -8.168 0.000 -1.182904 -.7241762
ldist | -.1343401 .0431032 -3.117 0.002 -.2190255 -.0496548
rooms | .2545271 .0185303 13.736 0.000 .2181203 .2909338
stratio | -.0524512 .0058971 -8.894 0.000 -.0640373 -.0408651
_cons | 11.08387 .3181115 34.843 0.000 10.45887 11.70886
------------------------------------------------------------------------------
Example 4.6: Participation Rates in 401K Plans
use http://fmwww.bc.edu/ec-p/data/wooldridge/401k
reg prate mrate age totemp
Source | SS df MS Number of obs = 1534
---------+------------------------------ F( 3, 1530) = 56.41
Model | 42666.5732 3 14222.1911 Prob > F = 0.0000
Residual | 385718.966 1530 252.103899 R-squared = 0.0996
---------+------------------------------ Adj R-squared = 0.0978
Total | 428385.539 1533 279.442622 Root MSE = 15.878
------------------------------------------------------------------------------
prate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
mrate | 5.441433 .5244086 10.376 0.000 4.412797 6.470068
age | .2694073 .0451486 5.967 0.000 .1808477 .3579669
totemp | -.0001298 .0000367 -3.535 0.000 -.0002018 -.0000578
_cons | 80.29429 .7776952 103.246 0.000 78.76882 81.81975
------------------------------------------------------------------------------
Change in participation rate if total employment increases by 10,000
display _b[totemp]*10000
-1.2978125
Example 4.7: Effect of Job Training Grants on Firm Scrap Rates
use http://fmwww.bc.edu/ec-p/data/wooldridge/jtrain
summ hrsemp sales employ
Variable | Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
hrsemp | 390 14.96754 25.71064 0 163.9167
sales | 373 6116327 7912603 110000 5.40e+07
employ | 440 59.31591 74.12378 4 525
reg lscrap hrsemp lsales lemploy
Source | SS df MS Number of obs = 135
---------+------------------------------ F( 3, 131) = 4.66
Model | 27.3075334 3 9.10251115 Prob > F = 0.0040
Residual | 256.148694 131 1.95533354 R-squared = 0.0963
---------+------------------------------ Adj R-squared = 0.0756
Total | 283.456227 134 2.11534498 Root MSE = 1.3983
------------------------------------------------------------------------------
lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
hrsemp | -.0031172 .0045738 -0.682 0.497 -.0121651 .0059308
lsales | -.7265661 .2169671 -3.349 0.001 -1.155779 -.2973534
lemploy | .7457646 .2090992 3.567 0.001 .3321164 1.159413
_cons | 8.800996 2.716819 3.239 0.002 3.42648 14.17551
------------------------------------------------------------------------------
Change in Firm Scrap Rates if training per employee increases by 1 hour
display _b[hrsemp]*1
-.00311716
Change in Firm Scrap Rates if training per employee increases by 5 hour
display _b[hrsemp]*5
-.01558579
Note: the textbook example is based on an undocumented subset of this dataset.
Example 4.8: Hedonic Price Model for Houses
Dataset is not available
Example 4.9: Parents Education in a Birth Weight Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/bwght
reg bwght cigs parity faminc motheduc fatheduc
Source | SS df MS Number of obs = 1191
---------+------------------------------ F( 5, 1185) = 9.55
Model | 18705.5567 5 3741.11135 Prob > F = 0.0000
Residual | 464041.135 1185 391.595895 R-squared = 0.0387
---------+------------------------------ Adj R-squared = 0.0347
Total | 482746.692 1190 405.669489 Root MSE = 19.789
------------------------------------------------------------------------------
bwght | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
cigs | -.5959362 .1103479 -5.401 0.000 -.8124352 -.3794373
parity | 1.787603 .6594055 2.711 0.007 .493871 3.081336
faminc | .0560414 .0365616 1.533 0.126 -.0156913 .1277742
motheduc | -.3704503 .3198551 -1.158 0.247 -.9979957 .2570951
fatheduc | .4723944 .2826433 1.671 0.095 -.0821426 1.026931
_cons | 114.5243 3.728453 30.716 0.000 107.2092 121.8394
------------------------------------------------------------------------------
Test for joint significance of motheduc and fatheduc
test motheduc fatheduc
( 1) motheduc = 0.0
( 2) fatheduc = 0.0
F( 2, 1185) = 1.44
Prob > F = 0.2380
reg bwght cigs parity faminc if e(sample)
Source | SS df MS Number of obs = 1191
-------------+------------------------------ F( 3, 1187) = 14.95
Model | 17579.8997 3 5859.96658 Prob > F = 0.0000
Residual | 465166.792 1187 391.884408 R-squared = 0.0364
-------------+------------------------------ Adj R-squared = 0.0340
Total | 482746.692 1190 405.669489 Root MSE = 19.796
------------------------------------------------------------------------------
bwght | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cigs | -.5978519 .1087701 -5.50 0.000 -.8112549 -.3844489
parity | 1.832274 .6575402 2.79 0.005 .5422035 3.122345
faminc | .0670618 .0323938 2.07 0.039 .0035063 .1306173
_cons | 115.4699 1.655898 69.73 0.000 112.2211 118.7187
------------------------------------------------------------------------------
Example 4.10: Salary-Pension Tradeoff for Teachers
use http://fmwww.bc.edu/ec-p/data/wooldridge/meap93
reg lsalary bensal lenrol lstaff droprate gradrate
Source | SS df MS Number of obs = 408
---------+------------------------------ F( 5, 402) = 45.43
Model | 3.49912092 5 .699824185 Prob > F = 0.0000
Residual | 6.19292056 402 .015405275 R-squared = 0.3610
---------+------------------------------ Adj R-squared = 0.3531
Total | 9.69204149 407 .02381337 Root MSE = .12412
------------------------------------------------------------------------------
lsalary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
bensal | -.5893175 .1648739 -3.574 0.000 -.9134402 -.2651948
lenroll | .0881206 .007324 12.032 0.000 .0737224 .1025187
lstaff | -.2182771 .0499504 -4.370 0.000 -.3164737 -.1200806
droprate | -.0002826 .0016145 -0.175 0.861 -.0034565 .0028913
gradrate | .0009674 .0006625 1.460 0.145 -.0003351 .0022699
_cons | 10.73846 .2582652 41.579 0.000 10.23074 11.24618
------------------------------------------------------------------------------
reg lsalary bensal lenrol lstaff
Source | SS df MS Number of obs = 408
---------+------------------------------ F( 3, 404) = 73.39
Model | 3.41865698 3 1.13955233 Prob > F = 0.0000
Residual | 6.27338451 404 .015528179 R-squared = 0.3527
---------+------------------------------ Adj R-squared = 0.3479
Total | 9.69204149 407 .02381337 Root MSE = .12461
------------------------------------------------------------------------------
lsalary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
bensal | -.6047698 .1653685 -3.657 0.000 -.9298599 -.2796797
lenroll | .0873968 .0073462 11.897 0.000 .0729552 .1018385
lstaff | -.2220324 .0500774 -4.434 0.000 -.3204773 -.1235875
_cons | 10.84383 .2516434 43.092 0.000 10.34914 11.33853
------------------------------------------------------------------------------
reg lsalary bensal
Source | SS df MS Number of obs = 408
---------+------------------------------ F( 1, 406) = 17.05
Model | .390608607 1 .390608607 Prob > F = 0.0000
Residual | 9.30143288 406 .022909933 R-squared = 0.0403
---------+------------------------------ Adj R-squared = 0.0379
Total | 9.69204149 407 .02381337 Root MSE = .15136
------------------------------------------------------------------------------
lsalary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
bensal | -.8253933 .199895 -4.129 0.000 -1.218352 -.4324349
_cons | 10.52318 .0415602 253.203 0.000 10.44148 10.60488
------------------------------------------------------------------------------
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