Cigarette Consumption Data
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).
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")
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