USGasG {AER} | R Documentation

## US Gasoline Market Data (1960–1995, Greene)

### Description

Time series data on the US gasoline market.

### Usage

    data("USGasG")

### 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.

### Source

Online complements to Greene (2003). Table F2.2.

http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

### References

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

### See Also

`Greene2003`, `USGasB`

### Examples

    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")

[Package AER version 1.2-5 Index]