Grunfeld {AER} | R Documentation |
Panel data on 11 large US manufacturing firms over 20 years, for the years 1935–1954.
data("Grunfeld")
A data frame containing 20 annual observations on 3 variables for 11 firms.
"General Motors"
, "US Steel"
, "General Electric"
,
"Chrysler"
, "Atlantic Refining"
, "IBM"
, "Union Oil"
, "Westinghouse"
,
"Goodyear"
, "Diamond Match"
, "American Steel"
.
This is a popular data set for teaching purposes. Unfortunately, there exist several
different versions (see http://www.stanford.edu/~clint/bench/grunfeld.htm for details).
In particular, the variants given by Baltagi (2002) and Greene (2003) differ:
for "US Steel"
(firm 2), Greene has investment for the year 1940 as 261.6 while Baltagi has 361.6,
and capital for 1946 as 132.6 while Baltagi has 232.6.
Here, we provide the original data from Grunfeld (1958, Table 9). The data for the first 10 firms is identical to that of Baltagi (2002), now also used by Greene (2008).
The data are taken from Grunfeld (1958, Table 9).
The data for the first 10 firms is identical to that of Baltagi (2002) 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. (2005). Econometric Analysis of Panel Data, 3rd ed. Chichester, UK: John Wiley.
Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.
Greene, W.H. (2008). Econometric Analysis, 6th edition. Upper Saddle River, NJ: Prentice Hall.
Grunfeld, Y. (1958). The Determinants of Corporate Investment. Unpublished Ph.D. Dissertation, University of Chicago.
data("Grunfeld") ## Greene (2003) ## subset of data with mistakes ggr <- subset(Grunfeld, firm %in% c("General Motors", "US Steel", "General Electric", "Chrysler", "Westinghouse")) ggr[c(26, 38), 1] <- c(261.6, 645.2) ggr[32, 3] <- 232.6 ## Tab. 14.2, col. "GM" fm_gm <- lm(invest ~ value + capital, data = ggr, subset = firm == "General Motors") mean(residuals(fm_gm)^2) ## Greene uses MLE ## Tab. 14.2, col. "Pooled" fm_pool <- lm(invest ~ value + capital, data = ggr) ## equivalently library("plm") pggr <- plm.data(ggr, c("firm", "year")) library("systemfit") fm_ols <- systemfit(invest ~ value + capital, data = pggr, method = "OLS") fm_pols <- systemfit(invest ~ value + capital, data = pggr, method = "OLS", pooled = TRUE) ## Tab. 14.1 fm_sur <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", methodResidCov = "noDfCor") fm_psur <- systemfit(invest ~ value + capital, data = pggr, method = "SUR", pooled = TRUE, methodResidCov = "noDfCor", residCovWeighted = TRUE) ## More examples can be found in: ## help("Greene2003") ## Panel models library("plm") pg <- plm.data(subset(Grunfeld, firm != "American Steel"), c("firm", "year")) fm_fe <- plm(invest ~ value + capital, model = "within", data = pg) summary(fm_fe) coeftest(fm_fe, vcov = pvcovHC) fm_reswar <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "swar") summary(fm_reswar) ## testing for random effects fm_ols <- plm(invest ~ value + capital, data = pg, model = "pooling") plmtest(fm_ols, type = "bp") plmtest(fm_ols, type = "honda") ## Random effects models fm_ream <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "amemiya") fm_rewh <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "walhus") fm_rener <- plm(invest ~ value + capital, data = pg, model = "random", random.method = "nerlove") ## Baltagi (2003), Tab. 2.1 rbind( "OLS(pooled)" = coef(fm_ols), "FE" = c(NA, coef(fm_fe)), "RE-SwAr" = coef(fm_reswar), "RE-Amemiya" = coef(fm_ream), "RE-WalHus" = coef(fm_rewh), "RE-Nerlove" = coef(fm_rener)) ## Hausman test phtest(fm_fe, fm_reswar) ## More examples can be found in: ## help("Baltagi2002") ## help("Greene2003")