R: US Consumption Data (1950-1993)

USConsump1993 {AER} | R Documentation

## US Consumption Data (1950–1993)

### Description

Time series data on US income and consumption expenditure, 1950–1993.

### Usage

    data("USConsump1993")

### Format

An annual multiple time series from 1950 to 1993 with 2 variables.

income

  Disposable personal income (in 1987 USD).

expenditure

  Personal consumption expenditures (in 1987 USD).

### Source

The data is from Baltagi (2002).

### References

Baltagi, B.H. (2002). Econometrics, 3rd ed. Berlin, Springer.

### See Also

`Baltagi2002`, `USConsump1950`, `USConsump1979`

### Examples

    ## data from Baltagi (2002)
    data("USConsump1993", package = "AER")
    plot(USConsump1993, plot.type = "single", col = 1:2)

    ## Chapter 5 (p. 122-125)
    fm <- lm(expenditure ~ income, data = USConsump1993)
    summary(fm)
    ## Durbin-Watson test (p. 122)
    dwtest(fm)
    ## Breusch-Godfrey test (Table 5.4, p. 124)
    bgtest(fm)
    ## Newey-West standard errors (Table 5.5, p. 125)
    coeftest(fm, vcov = NeweyWest(fm, lag = 3, prewhite = FALSE, adjust = TRUE))

    ## Chapter 8
    library("strucchange")
    ## Recursive residuals
    rr <- recresid(fm)
    rr
    ## Recursive CUSUM test
    rcus <- efp(expenditure ~ income, data = USConsump1993)
    plot(rcus)
    sctest(rcus)
    ## Harvey-Collier test
    harvtest(fm)
    ## NOTE" Mistake in Baltagi (2002) who computes
    ## the t-statistic incorrectly as 0.0733 via
    mean(rr)/sd(rr)/sqrt(length(rr))
    ## whereas it should be (as in harvtest)
    mean(rr)/sd(rr) * sqrt(length(rr))

    ## Rainbow test
    raintest(fm, center = 23)

    ## J test for non-nested models
    library("dynlm")
    fm1 <- dynlm(expenditure ~ income + L(income), data = USConsump1993)
    fm2 <- dynlm(expenditure ~ income + L(expenditure), data = USConsump1993)
    jtest(fm1, fm2)

    ## Chapter 14
    ## ACF and PACF for expenditures and first differences
    exps <- USConsump1993[, "expenditure"]
    (acf(exps))
    (pacf(exps))
    (acf(diff(exps)))
    (pacf(diff(exps)))

    ## dynamic regressions, eq. (14.8)
    fm <- dynlm(d(exps) ~ I(time(exps) - 1949) + L(exps))
    summary(fm)

[Package AER version 1.2-5 Index]