CigarettesB {AER} | R Documentation

## Cigarette Consumption Data

### Description

Cross-section data on cigarette consumption for 46 US States, for the year 1992.

### Usage

    data("CigarettesB")

### Format

A data frame containing 46 observations on 3 variables.

packs

  Logarithm of cigarette consumption (in packs) per person of smoking age (> 16 years).

price

  Logarithm of real price of cigarette in each state.

income

  Logarithm of real disposable income (per capita) in each state.

### Source

The data are from Baltagi (2002).

### References

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

Baltagi, B.H. and Levin, D. (1992). Cigarette Taxation: Raising Revenues and Reducing Consumption. Structural Change and Economic Dynamics, 3, 321–335.

### See Also

`Baltagi2002`, `CigarettesSW`

### Examples

    data("CigarettesB")

    ## Baltagi (2002)
    ## Table 3.3
    cig_lm <- lm(packs ~ price, data = CigarettesB)
    summary(cig_lm)

    ## Chapter 5: diagnostic tests (p. 111-115)
    cig_lm2 <- lm(packs ~ price + income, data = CigarettesB)
    summary(cig_lm2)
    ## Glejser tests (p. 112)
    ares <- abs(residuals(cig_lm2))
    summary(lm(ares ~ income, data = CigarettesB))
    summary(lm(ares ~ I(1/income), data = CigarettesB))
    summary(lm(ares ~ I(1/sqrt(income)), data = CigarettesB))
    summary(lm(ares ~ sqrt(income), data = CigarettesB))
    ## Goldfeld-Quandt test (p. 112)
    gqtest(cig_lm2, order.by = ~ income, data = CigarettesB, fraction = 12, alternative = "less")
    ## NOTE: Baltagi computes the test statistic as mss1/mss2,
    ## i.e., tries to find decreasing variances. gqtest() always uses
    ## mss2/mss1 and has an "alternative" argument.

    ## Spearman rank correlation test (p. 113)
    cor.test(~ ares + income, data = CigarettesB, method = "spearman")
    ## Breusch-Pagan test (p. 113)
    bptest(cig_lm2, varformula = ~ income, data = CigarettesB, student = FALSE)
    ## White test (Table 5.1, p. 113)
    bptest(cig_lm2, ~ income * price + I(income^2) + I(price^2), data = CigarettesB)
    ## White HC standard errors (Table 5.2, p. 114)
    coeftest(cig_lm2, vcov = vcovHC(cig_lm2, type = "HC1"))
    ## Jarque-Bera test (Figure 5.2, p. 115)
    hist(residuals(cig_lm2), breaks = 16, ylim = c(0, 10), col = "lightgray")
    library("tseries")
    jarque.bera.test(residuals(cig_lm2))

    ## Tables 8.1 and 8.2
    influence.measures(cig_lm2)

    ## More examples can be found in:
    ## help("Baltagi2002")

[Package AER version 1.2-5 Index]