CigarettesB {AER} | R Documentation |
Cross-section data on cigarette consumption for 46 US States, for the year 1992.
data("CigarettesB")
A data frame containing 46 observations on 3 variables.
Logarithm of cigarette consumption (in packs) per person of smoking age (> 16 years).
Logarithm of real price of cigarette in each state.
Logarithm of real disposable income (per capita) in each state.
The data are from Baltagi (2002) and available at
http://www.springeronline.com/sgw/cda/frontpage/0,10735,4-165-2-107420-0,00.html
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.
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")