Example 9.1: Economic Model of Crime
use http://fmwww.bc.edu/ec-p/data/wooldridge/crime1
reg narr86 pcnv avgsen tottime ptime86 qemp86 inc86 black hispan
Source | SS df MS Number of obs = 2725
-------------+------------------------------ F( 8, 2716) = 26.47
Model | 145.390104 8 18.173763 Prob > F = 0.0000
Residual | 1864.95705 2716 .686655763 R-squared = 0.0723
-------------+------------------------------ Adj R-squared = 0.0696
Total | 2010.34716 2724 .738012906 Root MSE = .82865
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.1332344 .0403502 -3.30 0.001 -.2123546 -.0541141
avgsen | -.0113177 .0122401 -0.92 0.355 -.0353185 .0126831
tottime | .0120224 .0094352 1.27 0.203 -.0064785 .0305233
ptime86 | -.0408417 .008812 -4.63 0.000 -.0581206 -.0235627
qemp86 | -.0505398 .0144397 -3.50 0.000 -.0788538 -.0222258
inc86 | -.0014887 .0003406 -4.37 0.000 -.0021566 -.0008207
black | .3265035 .0454156 7.19 0.000 .2374508 .4155561
hispan | .1939144 .0397113 4.88 0.000 .1160469 .2717818
_cons | .5686855 .0360461 15.78 0.000 .4980048 .6393661
------------------------------------------------------------------------------
reg narr86 pcnv pcnvsq avgsen tottime ptime86 pt86sq qemp86 inc86 inc86sq black hispan
Source | SS df MS Number of obs = 2725
-------------+------------------------------ F( 11, 2713) = 28.46
Model | 207.979007 11 18.9071825 Prob > F = 0.0000
Residual | 1802.36815 2713 .66434506 R-squared = 0.1035
-------------+------------------------------ Adj R-squared = 0.0998
Total | 2010.34716 2724 .738012906 Root MSE = .81507
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | .5525236 .1542372 3.58 0.000 .2500892 .8549579
pcnvsq | -.7302119 .1561177 -4.68 0.000 -1.036333 -.4240903
avgsen | -.0170216 .0120539 -1.41 0.158 -.0406574 .0066142
tottime | .011954 .0092825 1.29 0.198 -.0062474 .0301554
ptime86 | .2874334 .0442582 6.49 0.000 .2006501 .3742166
pt86sq | -.0296076 .0038634 -7.66 0.000 -.037183 -.0220321
qemp86 | -.0140941 .0173612 -0.81 0.417 -.0481366 .0199485
inc86 | -.0034152 .0008037 -4.25 0.000 -.0049912 -.0018392
inc86sq | 7.19e-06 2.56e-06 2.81 0.005 2.17e-06 .0000122
black | .292296 .04483 6.52 0.000 .2043916 .3802004
hispan | .1636175 .0394507 4.15 0.000 .0862609 .240974
_cons | .5046065 .0368353 13.70 0.000 .4323784 .5768347
------------------------------------------------------------------------------
Example 9.2: Housing Price Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/hprice1
reg price lotsize sqrft bdrms
Source | SS df MS Number of obs = 88
-------------+------------------------------ F( 3, 84) = 57.46
Model | 617130.701 3 205710.234 Prob > F = 0.0000
Residual | 300723.805 84 3580.0453 R-squared = 0.6724
-------------+------------------------------ Adj R-squared = 0.6607
Total | 917854.506 87 10550.0518 Root MSE = 59.833
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lotsize | .0020677 .0006421 3.22 0.002 .0007908 .0033446
sqrft | .1227782 .0132374 9.28 0.000 .0964541 .1491022
bdrms | 13.85252 9.010145 1.54 0.128 -4.06514 31.77018
_cons | -21.77031 29.47504 -0.74 0.462 -80.38466 36.84404
------------------------------------------------------------------------------
predict double r1
gen double r2=r1*r1
gen double r3=r2*r1
reg price lotsize sqrft bdrms r2 r3
Source | SS df MS Number of obs = 88
-------------+------------------------------ F( 5, 82) = 39.35
Model | 647870.698 5 129574.14 Prob > F = 0.0000
Residual | 269983.807 82 3292.48546 R-squared = 0.7059
-------------+------------------------------ Adj R-squared = 0.6879
Total | 917854.506 87 10550.0518 Root MSE = 57.38
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lotsize | .0001537 .005203 0.03 0.977 -.0101968 .0105042
sqrft | .0175989 .2992508 0.06 0.953 -.5777064 .6129041
bdrms | 2.174905 33.88811 0.06 0.949 -65.23934 69.58915
r2 | .0003534 .0070989 0.05 0.960 -.0137686 .0144755
r3 | 1.55e-06 6.55e-06 0.24 0.814 -.0000115 .0000146
_cons | 166.0973 317.4325 0.52 0.602 -465.3772 797.5717
------------------------------------------------------------------------------
test r2 r3
( 1) r2 = 0.0
( 2) r3 = 0.0
F( 2, 82) = 4.67
Prob > F = 0.0120
reg lprice llotsize lsqrft bdrms
Source | SS df MS Number of obs = 88
-------------+------------------------------ F( 3, 84) = 50.42
Model | 5.15504028 3 1.71834676 Prob > F = 0.0000
Residual | 2.86256324 84 .034078134 R-squared = 0.6430
-------------+------------------------------ Adj R-squared = 0.6302
Total | 8.01760352 87 .092156362 Root MSE = .1846
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llotsize | .1679667 .0382812 4.39 0.000 .0918404 .244093
lsqrft | .7002324 .0928652 7.54 0.000 .5155597 .8849051
bdrms | .0369584 .0275313 1.34 0.183 -.0177906 .0917074
_cons | -1.297042 .6512836 -1.99 0.050 -2.592191 -.0018931
------------------------------------------------------------------------------
predict lphat
gen lph2=lphat*lphat
gen lph3=lphat*lph2
reg lprice llotsize lsqrft bdrms lph2 lph3
Source | SS df MS Number of obs = 88
-------------+------------------------------ F( 5, 82) = 32.41
Model | 5.32360126 5 1.06472025 Prob > F = 0.0000
Residual | 2.69400226 82 .032853686 R-squared = 0.6640
-------------+------------------------------ Adj R-squared = 0.6435
Total | 8.01760352 87 .092156362 Root MSE = .18126
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llotsize | -4.191584 12.59578 -0.33 0.740 -29.2486 20.86543
lsqrft | -17.39336 52.49227 -0.33 0.741 -121.8172 87.0305
bdrms | -.9276645 2.76988 -0.33 0.739 -6.437838 4.582509
lph2 | 3.921189 13.01484 0.30 0.764 -21.96948 29.81186
lph3 | -.1933951 .7521095 -0.26 0.798 -1.68958 1.30279
_cons | 88.08799 240.9851 0.37 0.716 -391.3081 567.4841
------------------------------------------------------------------------------
test lph2 lph3
( 1) lph2 = 0.0
( 2) lph3 = 0.0
F( 2, 82) = 2.57
Prob > F = 0.0831
Example 9.3: IQ as a Price for Ability
use http://fmwww.bc.edu/ec-p/data/wooldridge/wage2
gen educIQ=educ*IQ
reg lwage educ exper tenure married south urban black
Source | SS df MS Number of obs = 935
-------------+------------------------------ F( 7, 927) = 44.75
Model | 41.8377677 7 5.97682396 Prob > F = 0.0000
Residual | 123.818527 927 .133569069 R-squared = 0.2526
-------------+------------------------------ Adj R-squared = 0.2469
Total | 165.656294 934 .177362199 Root MSE = .36547
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0654307 .0062504 10.47 0.000 .0531642 .0776973
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 .1227802 .2760541
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 -.1144282
_cons | 5.395497 .113225 47.65 0.000 5.17329 5.617704
------------------------------------------------------------------------------
reg lwage educ exper tenure married south urban black IQ
Source | SS df MS Number of obs = 935
-------------+------------------------------ F( 8, 926) = 41.27
Model | 43.5360229 8 5.44200287 Prob > F = 0.0000
Residual | 122.120271 926 .131879343 R-squared = 0.2628
-------------+------------------------------ Adj R-squared = 0.2564
Total | 165.656294 934 .177362199 Root MSE = .36315
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0544106 .0069285 7.85 0.000 .0408133 .068008
exper | .0141458 .0031651 4.47 0.000 .0079342 .0203575
tenure | .0113951 .0024394 4.67 0.000 .0066077 .0161825
married | .1997644 .0388025 5.15 0.000 .1236134 .2759154
south | -.0801695 .0262529 -3.05 0.002 -.1316916 -.0286473
urban | .1819463 .0267929 6.79 0.000 .1293645 .2345281
black | -.1431253 .0394925 -3.62 0.000 -.2206304 -.0656202
IQ | .0035591 .0009918 3.59 0.000 .0016127 .0055056
_cons | 5.176439 .1280006 40.44 0.000 4.925234 5.427644
------------------------------------------------------------------------------
reg lwage educ exper tenure married south urban black IQ educIQ
Source | SS df MS Number of obs = 935
-------------+------------------------------ F( 9, 925) = 36.76
Model | 43.6401304 9 4.84890337 Prob > F = 0.0000
Residual | 122.016164 925 .131909366 R-squared = 0.2634
-------------+------------------------------ Adj R-squared = 0.2563
Total | 165.656294 934 .177362199 Root MSE = .36319
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .0184558 .0410608 0.45 0.653 -.0621273 .099039
exper | .0139072 .0031768 4.38 0.000 .0076725 .0201418
tenure | .0113929 .0024397 4.67 0.000 .0066049 .0161808
married | .2008658 .0388267 5.17 0.000 .1246672 .2770644
south | -.0802354 .026256 -3.06 0.002 -.1317637 -.0287071
urban | .1835758 .0268586 6.83 0.000 .1308649 .2362867
black | -.1466989 .0397013 -3.70 0.000 -.2246139 -.0687839
IQ | -.0009418 .0051625 -0.18 0.855 -.0110734 .0091899
educIQ | .0003399 .0003826 0.89 0.375 -.0004109 .0010907
_cons | 5.648249 .5462963 10.34 0.000 4.576125 6.720373
------------------------------------------------------------------------------
Example 9.4: City Crime Rates
use http://fmwww.bc.edu/ec-p/data/wooldridge/crime2
reg lcrmrte unem llawexpc if d87==1
Source | SS df MS Number of obs = 46
-------------+------------------------------ F( 2, 43) = 1.30
Model | .271987199 2 .1359936 Prob > F = 0.2824
Residual | 4.48998214 43 .104418189 R-squared = 0.0571
-------------+------------------------------ Adj R-squared = 0.0133
Total | 4.76196934 45 .105821541 Root MSE = .32314
------------------------------------------------------------------------------
lcrmrte | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem | -.0290032 .0323387 -0.90 0.375 -.0942205 .0362141
llawexpc | .2033652 .1726534 1.18 0.245 -.1448236 .5515539
_cons | 3.342899 1.250527 2.67 0.011 .8209721 5.864826
------------------------------------------------------------------------------
reg lcrmrte unem llawexpc lcrmrt_1
Source | SS df MS Number of obs = 46
-------------+------------------------------ F( 3, 42) = 29.73
Model | 3.23732846 3 1.07910949 Prob > F = 0.0000
Residual | 1.52464088 42 .036300973 R-squared = 0.6798
-------------+------------------------------ Adj R-squared = 0.6570
Total | 4.76196934 45 .105821541 Root MSE = .19053
------------------------------------------------------------------------------
lcrmrte | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem | .008621 .0195166 0.44 0.661 -.0307652 .0480072
llawexpc | -.1395764 .1086412 -1.28 0.206 -.3588231 .0796704
lcrmrt_1 | 1.193923 .1320985 9.04 0.000 .9273371 1.460508
_cons | .0764511 .8211433 0.09 0.926 -1.580683 1.733585
------------------------------------------------------------------------------
Example 9.5: Saving Function with Measurement Error
Dataset is not provided
Example 9.6: Measurement Error in Scrap Rates
Dataset is not provided
Example 9.7: GPA Equation with Measurement Error
Dataset is not provided
Example 9.8: R&D Intensity and Firm Size
use http://fmwww.bc.edu/ec-p/data/wooldridge/rdchem
reg rdintens sales profmarg
Source | SS df MS Number of obs = 32
-------------+------------------------------ F( 2, 29) = 1.19
Model | 8.28423732 2 4.14211866 Prob > F = 0.3173
Residual | 100.549233 29 3.46721493 R-squared = 0.0761
-------------+------------------------------ Adj R-squared = 0.0124
Total | 108.83347 31 3.51075711 Root MSE = 1.862
------------------------------------------------------------------------------
rdintens | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0000534 .0000441 1.21 0.236 -.0000368 .0001435
profmarg | .0446166 .0461805 0.97 0.342 -.0498332 .1390664
_cons | 2.625261 .5855328 4.48 0.000 1.427712 3.82281
------------------------------------------------------------------------------
reg rdintens sales profmarg if sales<20000
Source | SS df MS Number of obs = 31
-------------+------------------------------ F( 2, 28) = 2.92
Model | 18.7880289 2 9.39401445 Prob > F = 0.0702
Residual | 89.9330615 28 3.21189505 R-squared = 0.1728
-------------+------------------------------ Adj R-squared = 0.1137
Total | 108.72109 30 3.62403635 Root MSE = 1.7922
------------------------------------------------------------------------------
rdintens | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0001856 .0000842 2.20 0.036 .0000131 .0003581
profmarg | .0478411 .0444831 1.08 0.291 -.0432784 .1389605
_cons | 2.296851 .5918045 3.88 0.001 1.084594 3.509107
------------------------------------------------------------------------------
Example 9.9: R&D Intensity
use http://fmwww.bc.edu/ec-p/data/wooldridge/rdchem
reg lrd lsales profmarg
Source | SS df MS Number of obs = 32
-------------+------------------------------ F( 2, 29) = 162.24
Model | 85.597056 2 42.798528 Prob > F = 0.0000
Residual | 7.6502049 29 .263800169 R-squared = 0.9180
-------------+------------------------------ Adj R-squared = 0.9123
Total | 93.2472609 31 3.00797616 Root MSE = .51361
------------------------------------------------------------------------------
lrd | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsales | 1.084228 .0601941 18.01 0.000 .9611173 1.207339
profmarg | .0216594 .012782 1.69 0.101 -.0044827 .0478015
_cons | -4.378349 .4680132 -9.36 0.000 -5.335544 -3.421155
------------------------------------------------------------------------------
reg lrd lsales profmarg if sales<20000
Source | SS df MS Number of obs = 31
-------------+------------------------------ F( 2, 28) = 131.42
Model | 71.7655416 2 35.8827708 Prob > F = 0.0000
Residual | 7.64489638 28 .273032014 R-squared = 0.9037
-------------+------------------------------ Adj R-squared = 0.8969
Total | 79.410438 30 2.6470146 Root MSE = .52252
------------------------------------------------------------------------------
lrd | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsales | 1.088057 .0671128 16.21 0.000 .9505826 1.225531
profmarg | .021759 .0130233 1.67 0.106 -.004918 .048436
_cons | -4.404225 .5110168 -8.62 0.000 -5.450995 -3.357454
------------------------------------------------------------------------------
Example 9.10: State Infant Mortality Rates
use http://fmwww.bc.edu/ec-p/data/wooldridge/infmrt
reg infmort lpcinc lphysic lpopul if year==1990
Source | SS df MS Number of obs = 51
-------------+------------------------------ F( 3, 47) = 2.53
Model | 32.1624527 3 10.7208176 Prob > F = 0.0684
Residual | 199.085016 47 4.23585141 R-squared = 0.1391
-------------+------------------------------ Adj R-squared = 0.0841
Total | 231.247469 50 4.62494938 Root MSE = 2.0581
------------------------------------------------------------------------------
infmort | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lpcinc | -4.684585 2.604134 -1.80 0.078 -9.923426 .5542562
lphysic | 4.153227 1.512663 2.75 0.009 1.110143 7.196312
lpopul | -.0878245 .2872503 -0.31 0.761 -.6656976 .4900486
_cons | 33.85875 20.42792 1.66 0.104 -7.236927 74.95444
------------------------------------------------------------------------------
reg infmort lpcinc lphysic lpopul if infmort<20 & year==1990
Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 3, 46) = 5.76
Model | 26.8600392 3 8.95334639 Prob > F = 0.0020
Residual | 71.4631627 46 1.55354702 R-squared = 0.2732
-------------+------------------------------ Adj R-squared = 0.2258
Total | 98.3232019 49 2.00659596 Root MSE = 1.2464
------------------------------------------------------------------------------
infmort | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lpcinc | -.5669247 1.641217 -0.35 0.731 -3.870523 2.736674
lphysic | -2.74184 1.190771 -2.30 0.026 -5.138737 -.344943
lpopul | .6292351 .1911062 3.29 0.002 .2445584 1.013912
_cons | 23.95478 12.41949 1.93 0.060 -1.044345 48.95391
------------------------------------------------------------------------------
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