# Data and Examples from Baltagi (2002) This manual page collects a list of examples from the book. Some solutions might not be exact and the list is certainly not complete. If you have suggestions for improvement (preferably in the form of code), please contact the package maintainer. ## References Baltagi, B.H. (2002). **Econometrics**, 3rd ed., Berlin: Springer-Verlag. ## See Also `BenderlyZwick`, `CigarettesB`, `EuroEnergy`, `Grunfeld`, `Mortgage`, `NaturalGas`, `OECDGas`, `OrangeCounty`, `PSID1982`, `TradeCredit`, `USConsump1993`, `USCrudes`, `USGasB`, `USMacroB` ## Examples ```r ################################ ## Cigarette consumption data ## ################################ ## data data("CigarettesB", package = "AER") ## Table 3.3 cig_lm <- lm(packs ~ price, data = CigarettesB) summary(cig_lm) ## Figure 3.9 plot(residuals(cig_lm) ~ price, data = CigarettesB) abline(h = 0, lty = 2) ## Figure 3.10 cig_pred <- with(CigarettesB, data.frame(price = seq(from = min(price), to = max(price), length = 30))) cig_pred <- cbind(cig_pred, predict(cig_lm, newdata = cig_pred, interval = "confidence")) plot(packs ~ price, data = CigarettesB) lines(fit ~ price, data = cig_pred) lines(lwr ~ price, data = cig_pred, lty = 2) lines(upr ~ price, data = cig_pred, lty = 2) ## 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) ##################################### ## US consumption data (1950-1993) ## ##################################### ## data 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 11 ## Table 11.1 Instrumental-variables regression usc <- as.data.frame(USConsump1993) usc$investment <- usc$income - usc$expenditure fm_ols <- lm(expenditure ~ income, data = usc) fm_iv <- ivreg(expenditure ~ income | investment, data = usc) ## Hausman test cf_diff <- coef(fm_iv) - coef(fm_ols) vc_diff <- vcov(fm_iv) - vcov(fm_ols) x2_diff <- as.vector(t(cf_diff) %*% solve(vc_diff) %*% cf_diff) pchisq(x2_diff, df = 2, lower.tail = FALSE) ## Chapter 14 ## ACF and PACF for expenditures and first differences exps <- USConsump1993[, "expenditure"] (acf(exps)) (pacf(exps)) (acf(diff(exps))) (pacf(diff(exps))) ## dynamic regressions, eq. (14.8) fm <- dynlm(d(exps) ~ I(time(exps) - 1949) + L(exps)) summary(fm) ################################ ## Grunfeld's investment data ## ################################ ## select the first three companies (as panel data) data("Grunfeld", package = "AER") pgr <- subset(Grunfeld, firm %in% levels(Grunfeld$firm)[1:3]) library("plm") pgr <- pdata.frame(pgr, c("firm", "year")) ## Ex. 10.9 library("systemfit") gr_ols <- systemfit(invest ~ value + capital, method = "OLS", data = pgr) gr_sur <- systemfit(invest ~ value + capital, method = "SUR", data = pgr) ######################################### ## Panel study on income dynamics 1982 ## ######################################### ## data data("PSID1982", package = "AER") ## Table 4.1 earn_lm <- lm(log(wage) ~ . + I(experience^2), data = PSID1982) summary(earn_lm) ## Table 13.1 union_lpm <- lm(I(as.numeric(union) - 1) ~ . - wage, data = PSID1982) union_probit <- glm(union ~ . - wage, data = PSID1982, family = binomial(link = "probit")) union_logit <- glm(union ~ . - wage, data = PSID1982, family = binomial) ## probit OK, logit and LPM rather different. ```

Baltagi2002 function