Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

CigarettesB

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

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")

AER

Applied Econometrics with R

v1.2-10
GPL-2 | GPL-3
Authors
Christian Kleiber [aut] (<https://orcid.org/0000-0002-6781-4733>), Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>)
Initial release
2022-06-13

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.