Grunfeld's Investment Data
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.
Gross investment, defined as additions to plant and equipment plus maintenance and repairs in millions of dollars deflated by the implicit price deflator of producers' durable equipment (base 1947).
Market value of the firm, defined as the price of common shares at December 31 (or, for WH, IBM and CH, the average price of December 31 and January 31 of the following year) times the number of common shares outstanding plus price of preferred shares at December 31 (or average price of December 31 and January 31 of the following year) times number of preferred shares plus total book value of debt at December 31 in millions of dollars deflated by the implicit GNP price deflator (base 1947).
Stock of plant and equipment, defined as the accumulated sum of net additions to plant and equipment deflated by the implicit price deflator for producers' durable equipment (base 1947) minus depreciation allowance deflated by depreciation expense deflator (10 years moving average of wholesale price index of metals and metal products, base 1947).
factor with 11 levels: "General Motors", "US Steel",
"General Electric", "Chrysler", "Atlantic Refining", "IBM",
"Union Oil", "Westinghouse", "Goodyear", "Diamond Match",
"American Steel".
Year.
This is a popular data set for teaching purposes. Unfortunately, there exist several
different versions (see Kleiber and Zeileis, 2010, for a detailed discussion).
In particular, the version provided by Greene (2003) has a couple of errors
for "US Steel" (firm 2):
investment in 1940 is 261.6 (instead of the correct 361.6),
investment in 1952 is 645.2 (instead of the correct 645.5),
capital in 1946 is 132.6 (instead of the correct 232.6).
Here, we provide the original data from Grunfeld (1958). The data for the first 10 firms are identical to those of Baltagi (2002) or Baltagi (2005), now also used by Greene (2008).
The data are taken from Grunfeld (1958, Appendix, Tables 2–9 and 11–13).
Baltagi, B.H. (2002). Econometrics, 3rd ed., Berlin: Springer-Verlag.
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.
Kleiber, C., and Zeileis, A. (2010). “The Grunfeld Data at 50.” German Economic Review, 11(4), 404–417. doi: 10.1111/j.1468-0475.2010.00513.x
data("Grunfeld", package = "AER")
## 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 <- pdata.frame(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)
## Further examples:
## help("Greene2003")
## Panel models
library("plm")
pg <- pdata.frame(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 = vcovHC)
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 (2005), 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)
## Further examples:
## help("Baltagi2002")
## help("Greene2003")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.