USGasG: US Gasoline Market Data (1960--1995, Greene)

Description

Time series data on the US gasoline market.

Usage

data("USGasG")

Arguments

Format

An annual multiple time series from 1960 to 1995 with 10 variables.

gas: Total US gasoline consumption (computed as total expenditure divided by price index).
price: Price index for gasoline.
income: Per capita disposable income.
newcar: Price index for new cars.
usedcar: Price index for used cars.
transport: Price index for public transportation.
durable: Aggregate price index for consumer durables.
nondurable: Aggregate price index for consumer nondurables.
service: Aggregate price index for consumer services.
population: US total population in millions.

References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

Examples

Run this code

 if(!requireNamespace("strucchange")) {
  if(interactive() || is.na(Sys.getenv("_R_CHECK_PACKAGE_NAME_", NA))) {
    stop("not all packages required for the example are installed")
  } else q() }
data("USGasG", package = "AER")
plot(USGasG)

## Greene (2003)
## Example 2.3
fm <- lm(log(gas/population) ~ log(price) + log(income) + log(newcar) + log(usedcar),
  data = as.data.frame(USGasG))
summary(fm)

## Example 4.4
## estimates and standard errors (note different offset for intercept)
coef(fm)
sqrt(diag(vcov(fm)))
## confidence interval
confint(fm, parm = "log(income)")
## test linear hypothesis
linearHypothesis(fm, "log(income) = 1")

## Example 7.6
## re-used in Example 8.3
trend <- 1:nrow(USGasG)
shock <- factor(time(USGasG) > 1973, levels = c(FALSE, TRUE),
  labels = c("before", "after"))

## 1960-1995
fm1 <- lm(log(gas/population) ~ log(income) + log(price) + log(newcar) +
  log(usedcar) + trend, data = as.data.frame(USGasG))
summary(fm1)
## pooled
fm2 <- lm(log(gas/population) ~ shock + log(income) + log(price) + log(newcar) +
  log(usedcar) + trend, data = as.data.frame(USGasG))
summary(fm2)
## segmented
fm3 <- lm(log(gas/population) ~ shock/(log(income) + log(price) + log(newcar) +
  log(usedcar) + trend), data = as.data.frame(USGasG))
summary(fm3)

## Chow test
anova(fm3, fm1)
library("strucchange")
sctest(log(gas/population) ~ log(income) + log(price) + log(newcar) +
  log(usedcar) + trend, data = USGasG, point = c(1973, 1), type = "Chow")
## Recursive CUSUM test
rcus <- efp(log(gas/population) ~ log(income) + log(price) + log(newcar) +
  log(usedcar) + trend, data = USGasG, type = "Rec-CUSUM")
plot(rcus)
sctest(rcus)
## Note: Greene's remark that the break is in 1984 (where the process crosses its
## boundary) is wrong. The break appears to be no later than 1976.

## More examples can be found in:
## help("Greene2003")