library(AER)
library(wooldridge)
Example 16.5
data("mroz")
s.iv.1 <- summary(iv.1 <- ivreg(hours ~ lwage + educ + age + kidslt6 + nwifeinc |
educ + age + kidslt6 + nwifeinc + exper + expersq,
data = mroz))
s.iv.1
##
## Call:
## ivreg(formula = hours ~ lwage + educ + age + kidslt6 + nwifeinc |
## educ + age + kidslt6 + nwifeinc + exper + expersq, data = mroz)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4570.13 -654.08 -36.94 569.86 8372.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2225.662 574.564 3.874 0.000124 ***
## lwage 1639.556 470.576 3.484 0.000545 ***
## educ -183.751 59.100 -3.109 0.002003 **
## age -7.806 9.378 -0.832 0.405664
## kidslt6 -198.154 182.929 -1.083 0.279325
## nwifeinc -10.170 6.615 -1.537 0.124942
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1354 on 422 degrees of freedom
## Multiple R-Squared: -2.008, Adjusted R-squared: -2.043
## Wald test: 3.441 on 5 and 422 DF, p-value: 0.004648
lm.1 <- lm(lwage ~ educ + age + kidslt6 + nwifeinc + exper + expersq,
data = mroz[mroz$inlf==1,])
lm.2 <- lm(hours ~ lwage + educ + age + kidslt6 + nwifeinc + lm.1$residuals,
data = mroz[mroz$inlf==1,])
summary(lm.2)
##
## Call:
## lm(formula = hours ~ lwage + educ + age + kidslt6 + nwifeinc +
## lm.1$residuals, data = mroz[mroz$inlf == 1, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1600.5 -548.6 107.7 461.4 3075.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2225.662 309.976 7.180 3.18e-12 ***
## lwage 1639.556 253.875 6.458 2.93e-10 ***
## educ -183.751 31.884 -5.763 1.60e-08 ***
## age -7.806 5.059 -1.543 0.12361
## kidslt6 -198.154 98.690 -2.008 0.04530 *
## nwifeinc -10.170 3.569 -2.850 0.00459 **
## lm.1$residuals -1714.358 259.439 -6.608 1.18e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 730.6 on 421 degrees of freedom
## Multiple R-squared: 0.1267, Adjusted R-squared: 0.1142
## F-statistic: 10.18 on 6 and 421 DF, p-value: 1.592e-10
Example 16.6
data("openness")
s.lm.1 <- summary(lm.1 <- lm(open ~ lpcinc + lland, data = openness))
s.lm.1
##
## Call:
## lm(formula = open ~ lpcinc + lland, data = openness)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.907 -8.843 -3.109 6.057 82.792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 117.0845 15.8483 7.388 2.97e-11 ***
## lpcinc 0.5465 1.4932 0.366 0.715
## lland -7.5671 0.8142 -9.294 1.51e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.8 on 111 degrees of freedom
## Multiple R-squared: 0.4487, Adjusted R-squared: 0.4387
## F-statistic: 45.17 on 2 and 111 DF, p-value: 4.451e-15
s.iv.1 <- summary(iv.1 <- ivreg(inf ~ open +lpcinc |
lpcinc + lland,
data = openness))
s.iv.1
##
## Call:
## ivreg(formula = inf ~ open + lpcinc | lpcinc + lland, data = openness)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.686 -10.176 -5.857 2.912 184.875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.8993 15.4012 1.747 0.0835 .
## open -0.3375 0.1441 -2.342 0.0210 *
## lpcinc 0.3758 2.0151 0.187 0.8524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.84 on 111 degrees of freedom
## Multiple R-Squared: 0.03088, Adjusted R-squared: 0.01341
## Wald test: 2.79 on 2 and 111 DF, p-value: 0.06574
Example 16.7
data("consump")
s.iv.1 <- summary(iv.1 <- ivreg(gc ~ gy + r3 |
gc_1 + gy_1 +r3_1,
data = consump))
s.iv.1
##
## Call:
## ivreg(formula = gc ~ gy + r3 | gc_1 + gy_1 + r3_1, data = consump)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0135627 -0.0035412 -0.0006202 0.0036514 0.0128639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0080597 0.0032327 2.493 0.018026 *
## gy 0.5861880 0.1345737 4.356 0.000128 ***
## r3 -0.0002694 0.0007640 -0.353 0.726698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.007471 on 32 degrees of freedom
## Multiple R-Squared: 0.6779, Adjusted R-squared: 0.6578
## Wald test: 9.586 on 2 and 32 DF, p-value: 0.0005468
Example 16.8
data("prison")
s.lm.1 <- summary(lm.1 <- lm(gcriv ~ y81 + y82 + y83 + y84 + y85 + y86 + y87 + y88 +
y89 + y90 + y91 + y92 + y93 + gpris + gpolpc + gincpc +
cunem + cblack + cmetro + cag0_14 + cag15_17 + cag18_24 +
cag25_34,
data = prison))
s.lm.1
##
## Call:
## lm(formula = gcriv ~ y81 + y82 + y83 + y84 + y85 + y86 + y87 +
## y88 + y89 + y90 + y91 + y92 + y93 + gpris + gpolpc + gincpc +
## cunem + cblack + cmetro + cag0_14 + cag15_17 + cag18_24 +
## cag25_34, data = prison)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32282 -0.04186 0.00283 0.04109 0.50580
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.005671 0.021558 -0.263 0.792597
## y81 -0.068626 0.017409 -3.942 8.91e-05 ***
## y82 -0.040773 0.020196 -2.019 0.043894 *
## y83 -0.042178 0.019804 -2.130 0.033550 *
## y84 -0.013660 0.022350 -0.611 0.541290
## y85 0.009404 0.020782 0.453 0.651037
## y86 0.044095 0.022736 1.939 0.052854 .
## y87 -0.023960 0.021713 -1.103 0.270212
## y88 0.034758 0.021613 1.608 0.108253
## y89 0.025357 0.021867 1.160 0.246603
## y90 0.087170 0.021185 4.115 4.35e-05 ***
## y91 0.038884 0.021359 1.821 0.069111 .
## y92 0.008150 0.022659 0.360 0.719183
## y93 0.008714 0.024004 0.363 0.716698
## gpris -0.180897 0.047628 -3.798 0.000159 ***
## gpolpc 0.051424 0.055516 0.926 0.354619
## gincpc 0.738368 0.166378 4.438 1.06e-05 ***
## cunem 0.411260 0.393670 1.045 0.296536
## cblack -0.014743 0.033157 -0.445 0.656708
## cmetro 0.538306 0.995660 0.541 0.588922
## cag0_14 0.989306 2.006539 0.493 0.622140
## cag15_17 4.983840 4.740475 1.051 0.293472
## cag18_24 2.412758 2.191017 1.101 0.271192
## cag25_34 2.879946 2.228829 1.292 0.196743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07893 on 690 degrees of freedom
## Multiple R-squared: 0.2311, Adjusted R-squared: 0.2055
## F-statistic: 9.019 on 23 and 690 DF, p-value: < 2.2e-16
s.iv.1 <- summary(iv.1 <- ivreg(gcriv ~ y81 + y82 + y83 + y84 + y85 + y86 + y87 + y88 +
y89 + y90 + y91 + y92 + y93 + gpris + gpolpc + gincpc +
cunem + cblack + cmetro + cag0_14 + cag15_17 + cag18_24 +
cag25_34 |
y81 + y82 + y83 + y84 + y85 + y86 + y87 + y88 +
y89 + y90 + y91 + y92 + y93 + gpolpc + gincpc +
cunem + cblack + cmetro + cag0_14 + cag15_17 + cag18_24 +
cag25_34 + final1 + final2,
data = prison))
s.iv.1
##
## Call:
## ivreg(formula = gcriv ~ y81 + y82 + y83 + y84 + y85 + y86 + y87 +
## y88 + y89 + y90 + y91 + y92 + y93 + gpris + gpolpc + gincpc +
## cunem + cblack + cmetro + cag0_14 + cag15_17 + cag18_24 +
## cag25_34 | y81 + y82 + y83 + y84 + y85 + y86 + y87 + y88 +
## y89 + y90 + y91 + y92 + y93 + gpolpc + gincpc + cunem + cblack +
## cmetro + cag0_14 + cag15_17 + cag18_24 + cag25_34 + final1 +
## final2, data = prison)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3863488 -0.0567073 -0.0002044 0.0509309 0.4133336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.014838 0.027520 0.539 0.58995
## y81 -0.056073 0.021735 -2.580 0.01009 *
## y82 0.028462 0.038477 0.740 0.45973
## y83 0.024703 0.037397 0.661 0.50911
## y84 0.012870 0.029334 0.439 0.66098
## y85 0.035403 0.027502 1.287 0.19844
## y86 0.092186 0.034388 2.681 0.00752 **
## y87 0.004771 0.029015 0.164 0.86944
## y88 0.053271 0.027322 1.950 0.05161 .
## y89 0.043086 0.027520 1.566 0.11790
## y90 0.144265 0.035462 4.068 5.29e-05 ***
## y91 0.061848 0.027650 2.237 0.02562 *
## y92 0.026657 0.028533 0.934 0.35050
## y93 0.022274 0.029610 0.752 0.45216
## gpris -1.031956 0.369963 -2.789 0.00543 **
## gpolpc 0.035315 0.067499 0.523 0.60101
## gincpc 0.910199 0.214327 4.247 2.47e-05 ***
## cunem 0.523696 0.478563 1.094 0.27420
## cblack -0.015848 0.040104 -0.395 0.69285
## cmetro -0.591517 1.298252 -0.456 0.64880
## cag0_14 3.379384 2.634893 1.283 0.20008
## cag15_17 3.549945 5.766302 0.616 0.53834
## cag18_24 3.358348 2.680839 1.253 0.21073
## cag25_34 2.319993 2.706345 0.857 0.39161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09547 on 690 degrees of freedom
## Multiple R-Squared: -0.1246, Adjusted R-squared: -0.1621
## Wald test: 6.075 on 23 and 690 DF, p-value: < 2.2e-16