Chapter13: Simple Panel Data Methods
library(wooldridge)Example 13.1
data("fertil1")
s.lm.1 <- summary(lm.1 <- lm(kids ~ educ + age + agesq + black + east + northcen + west +
farm + othrural + town + smcity +
y74 + y76 + y78 + y80 + y82 + y84, data = fertil1))
s.lm.1##
## Call:
## lm(formula = kids ~ educ + age + agesq + black + east + northcen +
## west + farm + othrural + town + smcity + y74 + y76 + y78 +
## y80 + y82 + y84, data = fertil1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9878 -1.0086 -0.0767 0.9331 4.6548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.742457 3.051767 -2.537 0.011315 *
## educ -0.128427 0.018349 -6.999 4.44e-12 ***
## age 0.532135 0.138386 3.845 0.000127 ***
## agesq -0.005804 0.001564 -3.710 0.000217 ***
## black 1.075658 0.173536 6.198 8.02e-10 ***
## east 0.217324 0.132788 1.637 0.101992
## northcen 0.363114 0.120897 3.004 0.002729 **
## west 0.197603 0.166913 1.184 0.236719
## farm -0.052557 0.147190 -0.357 0.721105
## othrural -0.162854 0.175442 -0.928 0.353481
## town 0.084353 0.124531 0.677 0.498314
## smcity 0.211879 0.160296 1.322 0.186507
## y74 0.268183 0.172716 1.553 0.120771
## y76 -0.097379 0.179046 -0.544 0.586633
## y78 -0.068666 0.181684 -0.378 0.705544
## y80 -0.071305 0.182771 -0.390 0.696511
## y82 -0.522484 0.172436 -3.030 0.002502 **
## y84 -0.545166 0.174516 -3.124 0.001831 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.555 on 1111 degrees of freedom
## Multiple R-squared: 0.1295, Adjusted R-squared: 0.1162
## F-statistic: 9.723 on 17 and 1111 DF, p-value: < 2.2e-16s.lm.2 <- summary(lm.2 <- lm(kids ~ educ + age + agesq + black + east + northcen + west +
farm + othrural + town + smcity,data = fertil1))
anova(lm.1, lm.2)## Analysis of Variance Table
##
## Model 1: kids ~ educ + age + agesq + black + east + northcen + west +
## farm + othrural + town + smcity + y74 + y76 + y78 + y80 +
## y82 + y84
## Model 2: kids ~ educ + age + agesq + black + east + northcen + west +
## farm + othrural + town + smcity
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1111 2685.9
## 2 1117 2771.0 -6 -85.139 5.8695 4.855e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Example 13.2
data("cps78_85")
s.lm.1 <- summary(lm.1 <- lm(lwage ~ y85 + educ + y85educ + exper + expersq +
union + female + y85fem, data = cps78_85))
s.lm.1##
## Call:
## lm(formula = lwage ~ y85 + educ + y85educ + exper + expersq +
## union + female + y85fem, data = cps78_85)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.56098 -0.25828 0.00864 0.26571 2.11669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.589e-01 9.345e-02 4.911 1.05e-06 ***
## y85 1.178e-01 1.238e-01 0.952 0.3415
## educ 7.472e-02 6.676e-03 11.192 < 2e-16 ***
## y85educ 1.846e-02 9.354e-03 1.974 0.0487 *
## exper 2.958e-02 3.567e-03 8.293 3.27e-16 ***
## expersq -3.994e-04 7.754e-05 -5.151 3.08e-07 ***
## union 2.021e-01 3.029e-02 6.672 4.03e-11 ***
## female -3.167e-01 3.662e-02 -8.648 < 2e-16 ***
## y85fem 8.505e-02 5.131e-02 1.658 0.0977 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4127 on 1075 degrees of freedom
## Multiple R-squared: 0.4262, Adjusted R-squared: 0.4219
## F-statistic: 99.8 on 8 and 1075 DF, p-value: < 2.2e-16Example 13.3
data("kielmc")
s.lm.1 <- summary(lm.1 <- lm(rprice ~ nearinc, data = kielmc[kielmc$year==1981,]))
s.lm.1##
## Call:
## lm(formula = rprice ~ nearinc, data = kielmc[kielmc$year == 1981,
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -60678 -19832 -2997 21139 136754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101308 3093 32.754 < 2e-16 ***
## nearinc -30688 5828 -5.266 5.14e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31240 on 140 degrees of freedom
## Multiple R-squared: 0.1653, Adjusted R-squared: 0.1594
## F-statistic: 27.73 on 1 and 140 DF, p-value: 5.139e-07s.lm.2 <- summary(lm.2 <- lm(rprice ~ nearinc, data = kielmc[kielmc$year == 1978,]))
s.lm.2##
## Call:
## lm(formula = rprice ~ nearinc, data = kielmc[kielmc$year == 1978,
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -56517 -16605 -3193 8683 236307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82517 2654 31.094 < 2e-16 ***
## nearinc -18824 4745 -3.968 0.000105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29430 on 177 degrees of freedom
## Multiple R-squared: 0.08167, Adjusted R-squared: 0.07648
## F-statistic: 15.74 on 1 and 177 DF, p-value: 0.0001054s.lm.3.1 <- summary(lm.3.1 <- lm(rprice ~ y81 + nearinc + y81nrinc, data = kielmc))
s.lm.3.1##
## Call:
## lm(formula = rprice ~ y81 + nearinc + y81nrinc, data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60678 -17693 -3031 12483 236307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82517 2727 30.260 < 2e-16 ***
## y81 18790 4050 4.640 5.12e-06 ***
## nearinc -18824 4875 -3.861 0.000137 ***
## y81nrinc -11864 7457 -1.591 0.112595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30240 on 317 degrees of freedom
## Multiple R-squared: 0.1739, Adjusted R-squared: 0.1661
## F-statistic: 22.25 on 3 and 317 DF, p-value: 4.224e-13s.lm.3.2 <- summary(lm.3.2 <- lm(rprice ~ y81 + nearinc + y81nrinc + age + agesq, data = kielmc))
s.lm.3.2##
## Call:
## lm(formula = rprice ~ y81 + nearinc + y81nrinc + age + agesq,
## data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79349 -14431 -1711 10069 201486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.912e+04 2.406e+03 37.039 < 2e-16 ***
## y81 2.132e+04 3.444e+03 6.191 1.86e-09 ***
## nearinc 9.398e+03 4.812e+03 1.953 0.051713 .
## y81nrinc -2.192e+04 6.360e+03 -3.447 0.000644 ***
## age -1.494e+03 1.319e+02 -11.333 < 2e-16 ***
## agesq 8.691e+00 8.481e-01 10.248 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25540 on 315 degrees of freedom
## Multiple R-squared: 0.4144, Adjusted R-squared: 0.4052
## F-statistic: 44.59 on 5 and 315 DF, p-value: < 2.2e-16s.lm.3.3 <- summary(lm.3.3 <- lm(rprice ~ y81 + nearinc + y81nrinc + age + agesq +
intst + land + area + rooms + baths, data = kielmc))
s.lm.3.3##
## Call:
## lm(formula = rprice ~ y81 + nearinc + y81nrinc + age + agesq +
## intst + land + area + rooms + baths, data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76721 -8885 -252 8433 136649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.381e+04 1.117e+04 1.237 0.21720
## y81 1.393e+04 2.799e+03 4.977 1.07e-06 ***
## nearinc 3.780e+03 4.453e+03 0.849 0.39661
## y81nrinc -1.418e+04 4.987e+03 -2.843 0.00477 **
## age -7.395e+02 1.311e+02 -5.639 3.85e-08 ***
## agesq 3.453e+00 8.128e-01 4.248 2.86e-05 ***
## intst -5.386e-01 1.963e-01 -2.743 0.00643 **
## land 1.414e-01 3.108e-02 4.551 7.69e-06 ***
## area 1.809e+01 2.306e+00 7.843 7.16e-14 ***
## rooms 3.304e+03 1.661e+03 1.989 0.04758 *
## baths 6.977e+03 2.581e+03 2.703 0.00725 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19620 on 310 degrees of freedom
## Multiple R-squared: 0.66, Adjusted R-squared: 0.6491
## F-statistic: 60.19 on 10 and 310 DF, p-value: < 2.2e-16s.lm.4.1 <- summary(lm.4.1 <- lm(lprice ~ y81 + nearinc + y81nrinc, data = kielmc))
s.lm.4.1##
## Call:
## lm(formula = lprice ~ y81 + nearinc + y81nrinc, data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.11957 -0.20328 0.02226 0.18909 1.66604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.28542 0.03051 369.839 < 2e-16 ***
## y81 0.45700 0.04532 10.084 < 2e-16 ***
## nearinc -0.33992 0.05456 -6.231 1.48e-09 ***
## y81nrinc -0.06265 0.08344 -0.751 0.453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3384 on 317 degrees of freedom
## Multiple R-squared: 0.4091, Adjusted R-squared: 0.4035
## F-statistic: 73.15 on 3 and 317 DF, p-value: < 2.2e-16s.lm.4.2 <- summary(lm.4.2 <- lm(lrprice ~ y81 + nearinc + y81nrinc + age + agesq +
lintst + lland + larea + rooms + baths, data = kielmc))
s.lm.4.2##
## Call:
## lm(formula = lrprice ~ y81 + nearinc + y81nrinc + age + agesq +
## lintst + lland + larea + rooms + baths, data = kielmc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18441 -0.09947 0.01478 0.10985 0.74872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.652e+00 4.159e-01 18.399 < 2e-16 ***
## y81 1.621e-01 2.850e-02 5.687 2.99e-08 ***
## nearinc 3.223e-02 4.749e-02 0.679 0.497805
## y81nrinc -1.315e-01 5.197e-02 -2.531 0.011885 *
## age -8.359e-03 1.411e-03 -5.924 8.36e-09 ***
## agesq 3.764e-05 8.668e-06 4.342 1.92e-05 ***
## lintst -6.145e-02 3.151e-02 -1.950 0.052045 .
## lland 9.985e-02 2.449e-02 4.077 5.81e-05 ***
## larea 3.508e-01 5.149e-02 6.813 4.98e-11 ***
## rooms 4.733e-02 1.733e-02 2.732 0.006661 **
## baths 9.428e-02 2.773e-02 3.400 0.000761 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2038 on 310 degrees of freedom
## Multiple R-squared: 0.7326, Adjusted R-squared: 0.7239
## F-statistic: 84.92 on 10 and 310 DF, p-value: < 2.2e-16Example 13.4
data("injury")
s.lm.1 <- summary(lm.1 <- lm(ldurat ~ afchnge + highearn + afhigh, data = injury))
s.lm.1##
## Call:
## lm(formula = ldurat ~ afchnge + highearn + afhigh, data = injury)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0128 -0.7214 -0.0171 0.7714 4.0047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.19934 0.02711 44.241 < 2e-16 ***
## afchnge 0.02364 0.03970 0.595 0.55164
## highearn 0.21520 0.04336 4.963 7.11e-07 ***
## afhigh 0.18835 0.06279 2.999 0.00271 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.298 on 7146 degrees of freedom
## Multiple R-squared: 0.01584, Adjusted R-squared: 0.01543
## F-statistic: 38.34 on 3 and 7146 DF, p-value: < 2.2e-16Hmmm, more observations in this estimation and the results differ…
Section 13.3
data("crime2")
s.lm.1 <- summary(lm.1 <- lm(crmrte ~ unem, data = crime2[crime2$year == 87,]))
s.lm.1##
## Call:
## lm(formula = crmrte ~ unem, data = crime2[crime2$year == 87,
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.55 -27.01 -10.56 18.01 79.75
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 128.378 20.757 6.185 1.8e-07 ***
## unem -4.161 3.416 -1.218 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.6 on 44 degrees of freedom
## Multiple R-squared: 0.03262, Adjusted R-squared: 0.01063
## F-statistic: 1.483 on 1 and 44 DF, p-value: 0.2297s.lm.2 <- summary(lm.2 <- lm(crmrte ~ d87 + unem, data = crime2))
s.lm.2##
## Call:
## lm(formula = crmrte ~ d87 + unem, data = crime2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.474 -21.794 -6.266 18.297 75.113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 93.4202 12.7395 7.333 9.92e-11 ***
## d87 7.9404 7.9753 0.996 0.322
## unem 0.4265 1.1883 0.359 0.720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.99 on 89 degrees of freedom
## Multiple R-squared: 0.01221, Adjusted R-squared: -0.009986
## F-statistic: 0.5501 on 2 and 89 DF, p-value: 0.5788Generate differences
crmrte.d <- crime2$crmrte[seq(2,length(crime2$unem), by = 2)] - crime2$crmrte[seq(1, (length(crime2$unem) - 1), by = 2)]
unem.d <- crime2$unem[seq(2, length(crime2$unem), by = 2)] - crime2$unem[seq(1, (length(crime2$unem) - 1), by = 2)]I suppose there is a better way to do this. I will revise it when I go througth the new edition of the textbook.
Estimate
s.lm.3 <- summary(lm.3 <- lm(crmrte.d ~ unem.d))
s.lm.3##
## Call:
## lm(formula = crmrte.d ~ unem.d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.912 -13.369 -5.507 12.446 52.915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.4022 4.7021 3.276 0.00206 **
## unem.d 2.2180 0.8779 2.527 0.01519 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.05 on 44 degrees of freedom
## Multiple R-squared: 0.1267, Adjusted R-squared: 0.1069
## F-statistic: 6.384 on 1 and 44 DF, p-value: 0.01519Example 13.5
data("slp75_81")
with(slp75_81, {
d.slpnap <- slpnap81-slpnap75
d.totwrk <- totwrk81-totwrk75
d.educ <- educ81-educ75
d.marr <- marr81-marr75
d.yngkid <- yngkid81-yngkid75
d.gdhlth <- gdhlth81-gdhlth75
summary(lm(d.slpnap ~ d.totwrk + d.educ + d.marr + d.yngkid + d.gdhlth))
})##
## Call:
## lm(formula = d.slpnap ~ d.totwrk + d.educ + d.marr + d.yngkid +
## d.gdhlth)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2454.2 -307.2 79.8 334.4 2037.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -92.63404 45.86590 -2.020 0.0446 *
## d.totwrk -0.22667 0.03605 -6.287 1.58e-09 ***
## d.educ -0.02447 48.75938 -0.001 0.9996
## d.marr 104.21395 92.85536 1.122 0.2629
## d.yngkid 94.66540 87.65252 1.080 0.2813
## d.gdhlth 87.57785 76.59913 1.143 0.2541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 598.6 on 233 degrees of freedom
## Multiple R-squared: 0.1495, Adjusted R-squared: 0.1313
## F-statistic: 8.191 on 5 and 233 DF, p-value: 3.827e-07Example 13.6
data("crime3")
with(crime3, {
d.lcrime <- lcrime[year==78] - lcrime[year==72]
d.clrprc1 <- clrprc1[year==78] - clrprc1[year==72]
d.clrprc2 <- clrprc2[year==78] - clrprc2[year==72]
summary(lm(d.lcrime ~ d.clrprc1 + d.clrprc2))
})##
## Call:
## lm(formula = d.lcrime ~ d.clrprc1 + d.clrprc2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0335 -0.2351 0.0299 0.2178 0.8263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.085656 0.063782 1.343 0.1854
## d.clrprc1 -0.004048 0.004720 -0.858 0.3952
## d.clrprc2 -0.013197 0.005195 -2.540 0.0142 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3446 on 50 degrees of freedom
## Multiple R-squared: 0.1933, Adjusted R-squared: 0.1611
## F-statistic: 5.992 on 2 and 50 DF, p-value: 0.004649Section 13.4
data("jtrain")
with(jtrain, {
d.scrap <- scrap[year == 1988] - scrap[year == 1987]
d.grant <- grant[year == 1988] - grant[year == 1987]
summary(lm(d.scrap ~ d.grant))
})##
## Call:
## lm(formula = d.scrap ~ d.grant)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4363 -0.0638 0.5437 0.8283 5.5637
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5637 0.4049 -1.392 0.170
## d.grant -0.7394 0.6826 -1.083 0.284
##
## Residual standard error: 2.396 on 52 degrees of freedom
## (103 observations deleted due to missingness)
## Multiple R-squared: 0.02207, Adjusted R-squared: 0.003261
## F-statistic: 1.173 on 1 and 52 DF, p-value: 0.2837With logs of scrap
with(jtrain, {
d.lscrap <- lscrap[year == 1988] - lscrap[year == 1987]
d.grant <- grant[year == 1988] - grant[year == 1987]
summary(lm(d.lscrap ~ d.grant))
})##
## Call:
## lm(formula = d.lscrap ~ d.grant)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1277 -0.2372 0.0237 0.2142 2.4553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05744 0.09721 -0.591 0.5572
## d.grant -0.31706 0.16388 -1.935 0.0585 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5751 on 52 degrees of freedom
## (103 observations deleted due to missingness)
## Multiple R-squared: 0.06715, Adjusted R-squared: 0.04921
## F-statistic: 3.743 on 1 and 52 DF, p-value: 0.05847Example 13.7
data("traffic1")
with(traffic1, {
d.dthrte <- dthrte90 - dthrte85
d.open <- open90 - open85
d.admn <- admn90 - admn85
summary(lm(d.dthrte ~ d.open + d.admn))
})##
## Call:
## lm(formula = d.dthrte ~ d.open + d.admn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.25261 -0.14337 -0.00321 0.19679 0.79679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.49679 0.05243 -9.476 1.43e-12 ***
## d.open -0.41968 0.20559 -2.041 0.0467 *
## d.admn -0.15060 0.11682 -1.289 0.2035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3435 on 48 degrees of freedom
## Multiple R-squared: 0.1187, Adjusted R-squared: 0.08194
## F-statistic: 3.231 on 2 and 48 DF, p-value: 0.04824Example 13.8
data("ezunem")
with(ezunem,{
d.luclms <- luclms[year == 1981] - luclms[year == 1980]
d.luclms <- append(d.luclms, luclms[year == 1982] - luclms[year == 1981])
d.luclms <- append(d.luclms, luclms[year == 1983] - luclms[year == 1982])
d.luclms <- append(d.luclms, luclms[year == 1984] - luclms[year == 1983])
d.luclms <- append(d.luclms, luclms[year == 1985] - luclms[year == 1984])
d.luclms <- append(d.luclms, luclms[year == 1986] - luclms[year == 1985])
d.luclms <- append(d.luclms, luclms[year == 1987] - luclms[year == 1986])
d.luclms <- append(d.luclms, luclms[year == 1988] - luclms[year == 1987])
d.ez <- ez[year == 1981] - ez[year == 1980]
d.ez <- append(d.ez, ez[year == 1982] - ez[year == 1981])
d.ez <- append(d.ez, ez[year == 1983] - ez[year == 1982])
d.ez <- append(d.ez, ez[year == 1984] - ez[year == 1983])
d.ez <- append(d.ez, ez[year == 1985] - ez[year == 1984])
d.ez <- append(d.ez, ez[year == 1986] - ez[year == 1985])
d.ez <- append(d.ez, ez[year == 1987] - ez[year == 1986])
d.ez <- append(d.ez, ez[year == 1988] - ez[year == 1987])
d.82 <- c()
for (i in 1981:1988) {
d.82 <- append(d.82,d82[year == i] - d82[year == (i - 1)])
}
d.83 <- c()
for (i in 1981:1988) {
d.83 <- append(d.83,d83[year == i] - d83[year == (i - 1)])
}
d.84 <- c()
for (i in 1981:1988) {
d.84 <- append(d.84,d84[year == i] - d84[year == (i - 1)])
}
d.85 <- c()
for (i in 1981:1988) {
d.85 <- append(d.85,d85[year == i] - d85[year == (i - 1)])
}
d.86 <- c()
for (i in 1981:1988) {
d.86 <- append(d.86,d86[year == i] - d86[year == (i - 1)])
}
d.87 <- c()
for (i in 1981:1988) {
d.87 <- append(d.87,d87[year == i] - d87[year == (i - 1)])
}
d.88 <- c()
for (i in 1981:1988) {
d.88 <- append(d.88,d88[year == i] - d88[year == (i - 1)])
}
summary(lm(d.luclms ~ d.ez + d.82 + d.83 + d.84 + d.85 + d.86 + d.87 + d.88))
})##
## Call:
## lm(formula = d.luclms ~ d.ez + d.82 + d.83 + d.84 + d.85 + d.86 +
## d.87 + d.88)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4925 -0.1427 -0.0092 0.1495 0.6062
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.32163 0.04606 -6.982 6.55e-11 ***
## d.ez -0.18188 0.07819 -2.326 0.021209 *
## d.82 0.77876 0.06514 11.954 < 2e-16 ***
## d.83 0.74564 0.11283 6.608 4.99e-10 ***
## d.84 0.72850 0.16099 4.525 1.14e-05 ***
## d.85 1.05158 0.20905 5.030 1.25e-06 ***
## d.86 1.34374 0.25479 5.274 4.09e-07 ***
## d.87 1.39769 0.30064 4.649 6.74e-06 ***
## d.88 1.38063 0.34654 3.984 0.000101 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2161 on 167 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6049
## F-statistic: 34.5 on 8 and 167 DF, p-value: < 2.2e-16Example 13.9
data("crime4")
with(crime4, {
d.lcrmrte <- c()
for (i in 82:87) {
d.lcrmrte <- append(d.lcrmrte, lcrmrte[year==i] - lcrmrte[year==(i - 1)])
}
d.lprbarr <- c()
for (i in 82:87) {
d.lprbarr <- append(d.lprbarr, lprbarr[year==i] - lprbarr[year==(i - 1)])
}
d.lprbconv <- c()
for (i in 82:87) {
d.lprbconv <- append(d.lprbconv, lprbconv[year==i] - lprbconv[year==(i - 1)])
}
d.lprbpris <- c()
for (i in 82:87) {
d.lprbpris <- append(d.lprbpris, lprbpris[year==i] - lprbpris[year==(i - 1)])
}
d.lavgsen <- c()
for (i in 82:87) {
d.lavgsen <- append(d.lavgsen, lavgsen[year==i] - lavgsen[year==(i - 1)])
}
d.lpolpc <- c()
for (i in 82:87) {
d.lpolpc <- append(d.lpolpc, lpolpc[year==i] - lpolpc[year==(i - 1)])
}
d.83 <- c()
for (i in 82:87) {
d.83 <- append(d.83, d83[year==i] - d83[year==(i - 1)])
}
d.84 <- c()
for (i in 82:87) {
d.84 <- append(d.84, d84[year==i] - d84[year==(i - 1)])
}
d.85 <- c()
for (i in 82:87) {
d.85 <- append(d.85, d85[year==i] - d85[year==(i - 1)])
}
d.86 <- c()
for (i in 82:87) {
d.86 <- append(d.86, d86[year==i] - d86[year==(i - 1)])
}
d.87 <- c()
for (i in 82:87) {
d.87 <- append(d.87, d87[year==i] - d87[year==(i - 1)])
}
summary(lm(d.lcrmrte ~ d.83 + d.84 + d.85 + d.86 + d.87 + d.lprbarr + d.lprbconv +
d.lprbpris + d.lavgsen + d.lpolpc))
})##
## Call:
## lm(formula = d.lcrmrte ~ d.83 + d.84 + d.85 + d.86 + d.87 + d.lprbarr +
## d.lprbconv + d.lprbpris + d.lavgsen + d.lpolpc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.65936 -0.07838 0.00296 0.07504 0.68307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.007713 0.017058 0.452 0.651320
## d.83 -0.099866 0.023895 -4.179 3.42e-05 ***
## d.84 -0.147803 0.041279 -3.581 0.000374 ***
## d.85 -0.152414 0.058400 -2.610 0.009315 **
## d.86 -0.124900 0.076004 -1.643 0.100909
## d.87 -0.084073 0.094000 -0.894 0.371518
## d.lprbarr -0.327494 0.029980 -10.924 < 2e-16 ***
## d.lprbconv -0.238107 0.018234 -13.058 < 2e-16 ***
## d.lprbpris -0.165046 0.025969 -6.356 4.49e-10 ***
## d.lavgsen -0.021761 0.022091 -0.985 0.325050
## d.lpolpc 0.398426 0.026882 14.821 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1543 on 529 degrees of freedom
## Multiple R-squared: 0.4325, Adjusted R-squared: 0.4218
## F-statistic: 40.32 on 10 and 529 DF, p-value: < 2.2e-16