Example 16.1: Murder Rates and Size of the Police Force
Dataset is not available
Example 16.2: Housing Expenditures and Saving
Dataset is not available
Example 16.4: Labor Supply of Married, Working Women
Dataset is not available
Example 16.4: Inflation and Openness
Dataset is not available
Example 16.5: Labor Supply of Married, Working Women
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz, clear
ivreg hours (lwage = exper expersq ) educ age kidslt6 nwifeinc
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 5, 422) = 3.44
Model | -516582090 5 -103316418 Prob > F = 0.0046
Residual | 773893110 422 1833869.93 R-squared = .
-------------+------------------------------ Adj R-squared = .
Total | 257311020 427 602601.92 Root MSE = 1354.2
------------------------------------------------------------------------------
hours | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lwage | 1639.556 470.5757 3.48 0.001 714.5914 2564.52
educ | -183.7513 59.09981 -3.11 0.002 -299.918 -67.58463
age | -7.806094 9.378013 -0.83 0.406 -26.23953 10.62734
kidslt6 | -198.1543 182.9291 -1.08 0.279 -557.72 161.4115
nwifeinc | -10.16959 6.614743 -1.54 0.125 -23.17154 2.832358
_cons | 2225.662 574.5641 3.87 0.000 1096.298 3355.026
------------------------------------------------------------------------------
Instrumented: lwage
Instruments: educ age kidslt6 nwifeinc exper expersq
------------------------------------------------------------------------------
reg hours lwage educ age kidslt6 nwifeinc
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 5, 422) = 3.16
Model | 9290528.53 5 1858105.71 Prob > F = 0.0082
Residual | 248020491 422 587726.283 R-squared = 0.0361
-------------+------------------------------ Adj R-squared = 0.0247
Total | 257311020 427 602601.92 Root MSE = 766.63
------------------------------------------------------------------------------
hours | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lwage | -2.0468 54.88014 -0.04 0.970 -109.9193 105.8257
educ | -6.621869 18.11627 -0.37 0.715 -42.23123 28.98749
age | .562254 5.140012 0.11 0.913 -9.540961 10.66547
kidslt6 | -328.8584 101.4573 -3.24 0.001 -528.2831 -129.4338
nwifeinc | -5.918458 3.683341 -1.61 0.109 -13.15844 1.321522
_cons | 1523.775 305.5755 4.99 0.000 923.1353 2124.414
------------------------------------------------------------------------------
ivreg lwage (hours = age kidslt6 nwifeinc) educ exper expersq
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 4, 423) = 19.03
Model | 28.0618854 4 7.01547135 Prob > F = 0.0000
Residual | 195.265566 423 .461620723 R-squared = 0.1257
-------------+------------------------------ Adj R-squared = 0.1174
Total | 223.327451 427 .523015108 Root MSE = .67943
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hours | .0001259 .0002546 0.49 0.621 -.0003746 .0006264
educ | .11033 .0155244 7.11 0.000 .0798155 .1408445
exper | .0345824 .0194916 1.77 0.077 -.00373 .0728947
expersq | -.0007058 .0004541 -1.55 0.121 -.0015983 .0001868
_cons | -.6557256 .3377883 -1.94 0.053 -1.319678 .008227
------------------------------------------------------------------------------
Instrumented: hours
Instruments: educ exper expersq age kidslt6 nwifeinc
------------------------------------------------------------------------------
Example 16.6: Inflation and Openness
use http://fmwww.bc.edu/ec-p/data/wooldridge/openness, clear
ivreg inf (open = lland) lpcinc, first
First-stage regressions
-----------------------
Source | SS df MS Number of obs = 114
-------------+------------------------------ F( 2, 111) = 45.17
Model | 28606.193 2 14303.0965 Prob > F = 0.0000
Residual | 35151.7973 111 316.682858 R-squared = 0.4487
-------------+------------------------------ Adj R-squared = 0.4387
Total | 63757.9902 113 564.230002 Root MSE = 17.796
------------------------------------------------------------------------------
open | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lpcinc | .5464794 1.49324 0.37 0.715 -2.412475 3.505433
lland | -7.567103 .8142162 -9.29 0.000 -9.180527 -5.953679
_cons | 117.0845 15.8483 7.39 0.000 85.68007 148.489
------------------------------------------------------------------------------
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 114
-------------+------------------------------ F( 2, 111) = 2.79
Model | 2009.2308 2 1004.6154 Prob > F = 0.0657
Residual | 63064.1909 111 568.145864 R-squared = 0.0309
-------------+------------------------------ Adj R-squared = 0.0134
Total | 65073.4217 113 575.870989 Root MSE = 23.836
------------------------------------------------------------------------------
inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
open | -.3374869 .1441212 -2.34 0.021 -.6230726 -.0519012
lpcinc | .3758232 2.015081 0.19 0.852 -3.617194 4.36884
_cons | 26.89934 15.4012 1.75 0.083 -3.619157 57.41784
------------------------------------------------------------------------------
Instrumented: open
Instruments: lpcinc lland
------------------------------------------------------------------------------
Example 16.7: Testing the Permanent Income Hypothesis
use http://fmwww.bc.edu/ec-p/data/wooldridge/consump, clear
tsset year
ivreg gc gy (r3 = L.gc L.gy L.r3)
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 35
-------------+------------------------------ F( 2, 32) = 33.68
Model | .003759528 2 .001879764 Prob > F = 0.0000
Residual | .001786069 32 .000055815 R-squared = 0.6779
-------------+------------------------------ Adj R-squared = 0.6578
Total | .005545597 34 .000163106 Root MSE = .00747
------------------------------------------------------------------------------
gc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
r3 | -.0002698 .0007639 -0.35 0.726 -.0018258 .0012861
gy | .5826032 .0747338 7.80 0.000 .4303755 .7348309
_cons | .0081396 .002054 3.96 0.000 .0039557 .0123236
------------------------------------------------------------------------------
Instrumented: r3
Instruments: gy L.gc L.gy L.r3
------------------------------------------------------------------------------
Example 16.8: Effect of Prison Population on Violent Crime Rates
use http://fmwww.bc.edu/ec-p/data/wooldridge/prison, clear
tsset state year
reg gcriv cag0_14 cag15_17 cag18_24 cag25_34 cunem cblack cmetro gincpc gpolpc gpris
Source | SS df MS Number of obs = 714
-------------+------------------------------ F( 10, 703) = 8.09
Model | .576975497 10 .05769755 Prob > F = 0.0000
Residual | 5.01453125 703 .007133046 R-squared = 0.1032
-------------+------------------------------ Adj R-squared = 0.0904
Total | 5.59150675 713 .007842225 Root MSE = .08446
------------------------------------------------------------------------------
gcriv | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cag0_14 | -2483.038 1400.61 -1.77 0.077 -5232.917 266.8405
cag15_17 | 10.79079 2.677477 4.03 0.000 5.533976 16.04759
cag18_24 | -.4464182 1.756685 -0.25 0.799 -3.895395 3.002558
cag25_34 | -4.329303 1.407631 -3.08 0.002 -7.092966 -1.56564
cunem | .0053237 .0027825 1.91 0.056 -.0001393 .0107867
cblack | -.0021635 .0358322 -0.06 0.952 -.0725144 .0681874
cmetro | .0018484 .0108955 0.17 0.865 -.0195432 .02324
gincpc | .9395616 .151253 6.21 0.000 .6425999 1.236523
gpolpc | .0854818 .0585893 1.46 0.145 -.0295491 .2005127
gpris | -.1739892 .0482266 -3.61 0.000 -.2686747 -.0793038
_cons | .0386684 .0335862 1.15 0.250 -.0272729 .1046097
------------------------------------------------------------------------------
ivreg gcriv cag0_14 cag15_17 cag18_24 cag25_34 cunem cblack cmetro gincpc gpolpc (gpris = final1 final2)
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 714
-------------+------------------------------ F( 10, 703) = 5.58
Model | -1.48643443 10 -.148643443 Prob > F = 0.0000
Residual | 7.07794118 703 .010068195 R-squared = .
-------------+------------------------------ Adj R-squared = .
Total | 5.59150675 713 .007842225 Root MSE = .10034
------------------------------------------------------------------------------
gcriv | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gpris | -.9942312 .3589068 -2.77 0.006 -1.698889 -.2895738
cag0_14 | -3138.017 1687.888 -1.86 0.063 -6451.922 175.8876
cag15_17 | 5.211254 3.990898 1.31 0.192 -2.624253 13.04676
cag18_24 | -2.638793 2.291848 -1.15 0.250 -7.138478 1.860893
cag25_34 | -5.737185 1.779489 -3.22 0.001 -9.230934 -2.243436
cunem | .008557 .0035887 2.38 0.017 .0015112 .0156027
cblack | -.003239 .0425733 -0.08 0.939 -.0868251 .080347
cmetro | -.0045437 .0132357 -0.34 0.731 -.03053 .0214425
gincpc | .9112354 .1801137 5.06 0.000 .5576101 1.264861
gpolpc | .0641088 .0702171 0.91 0.362 -.0737516 .2019692
_cons | .0987133 .047591 2.07 0.038 .0052758 .1921508
------------------------------------------------------------------------------
Instrumented: gpris
Instruments: cag0_14 cag15_17 cag18_24 cag25_34 cunem cblack cmetro gincpc
gpolpc final1 final2
------------------------------------------------------------------------------
This page prepared by Oleksandr Talavera
Send your questions/comments/suggestions to Kit Baum
at baum@bc.edu
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