1\name{USGasG} 2\alias{USGasG} 3\title{US Gasoline Market Data (1960--1995, Greene)} 4\description{ 5Time series data on the US gasoline market. 6} 7\usage{data("USGasG")} 8\format{ 9An annual multiple time series from 1960 to 1995 with 10 variables. 10 \describe{ 11 \item{gas}{Total US gasoline consumption (computed as 12 total expenditure divided by price index).} 13 \item{price}{Price index for gasoline.} 14 \item{income}{Per capita disposable income.} 15 \item{newcar}{Price index for new cars.} 16 \item{usedcar}{Price index for used cars.} 17 \item{transport}{Price index for public transportation.} 18 \item{durable}{Aggregate price index for consumer durables.} 19 \item{nondurable}{Aggregate price index for consumer nondurables.} 20 \item{service}{Aggregate price index for consumer services.} 21 \item{population}{US total population in millions.} 22 } 23} 24 25\source{ 26Online complements to Greene (2003). Table F2.2. 27 28\url{http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm} 29} 30 31\references{ 32Greene, W.H. (2003). \emph{Econometric Analysis}, 5th edition. Upper Saddle River, NJ: Prentice Hall. 33} 34 35\seealso{\code{\link{Greene2003}}, \code{\link{USGasB}}} 36 37\examples{ 38data("USGasG", package = "AER") 39plot(USGasG) 40 41## Greene (2003) 42## Example 2.3 43fm <- lm(log(gas/population) ~ log(price) + log(income) + log(newcar) + log(usedcar), 44 data = as.data.frame(USGasG)) 45summary(fm) 46 47## Example 4.4 48## estimates and standard errors (note different offset for intercept) 49coef(fm) 50sqrt(diag(vcov(fm))) 51## confidence interval 52confint(fm, parm = "log(income)") 53## test linear hypothesis 54linearHypothesis(fm, "log(income) = 1") 55 56## Example 7.6 57## re-used in Example 8.3 58trend <- 1:nrow(USGasG) 59shock <- factor(time(USGasG) > 1973, levels = c(FALSE, TRUE), 60 labels = c("before", "after")) 61 62## 1960-1995 63fm1 <- lm(log(gas/population) ~ log(income) + log(price) + log(newcar) + 64 log(usedcar) + trend, data = as.data.frame(USGasG)) 65summary(fm1) 66## pooled 67fm2 <- lm(log(gas/population) ~ shock + log(income) + log(price) + log(newcar) + 68 log(usedcar) + trend, data = as.data.frame(USGasG)) 69summary(fm2) 70## segmented 71fm3 <- lm(log(gas/population) ~ shock/(log(income) + log(price) + log(newcar) + 72 log(usedcar) + trend), data = as.data.frame(USGasG)) 73summary(fm3) 74 75## Chow test 76anova(fm3, fm1) 77library("strucchange") 78sctest(log(gas/population) ~ log(income) + log(price) + log(newcar) + 79 log(usedcar) + trend, data = USGasG, point = c(1973, 1), type = "Chow") 80## Recursive CUSUM test 81rcus <- efp(log(gas/population) ~ log(income) + log(price) + log(newcar) + 82 log(usedcar) + trend, data = USGasG, type = "Rec-CUSUM") 83plot(rcus) 84sctest(rcus) 85## Note: Greene's remark that the break is in 1984 (where the process crosses its 86## boundary) is wrong. The break appears to be no later than 1976. 87 88## More examples can be found in: 89## help("Greene2003") 90} 91 92\keyword{datasets} 93