##-------------------------------------------------------##
## An R Companion to Applied Regression, Second Edition  ##
##    Script for Appendix on Timeseries Regression       ##
##                                                       ##
##    John Fox and Sanford Weisberg                      ##
##    Sage Publications, 2011                            ##
##-------------------------------------------------------##

library(car)
Hartnagel[c(1:6, 36:38), ]
plot(fconvict ~ year, type="n",data=Hartnagel,
    ylab="Convictions per 100,000 Women")
grid(lty=1)
with(Hartnagel, points(year, fconvict, type="o", pch=16))
mod.ols <- lm(fconvict ~ tfr + partic + degrees + mconvict, data=Hartnagel)
summary(mod.ols)
plot(Hartnagel$year, residuals(mod.ols), type="o", pch=16,
    xlab="Year", ylab="OLS Residuals")
abline(h=0, lty=2)
acf(residuals(mod.ols))
acf(residuals(mod.ols), type="partial")
durbinWatsonTest(mod.ols, max.lag=5)
library(lmtest)
dwtest(mod.ols, alternative="two.sided") 
library(nlme)
mod.gls <- gls(fconvict ~ tfr + partic + degrees + mconvict,
    data=Hartnagel, correlation=corARMA(p=2), method="ML")
summary(mod.gls)
mod.gls.3 <- update(mod.gls, correlation=corARMA(p=3))
mod.gls.1 <- update(mod.gls, correlation=corARMA(p=1))
mod.gls.0 <- update(mod.gls, correlation=NULL)
anova(mod.gls, mod.gls.1)
anova(mod.gls, mod.gls.0)
anova(mod.gls.3, mod.gls)