AER (version 0.2-2)

Grunfeld: Grunfeld's Investment Data

Description

Panel data on 11 large US manufacturing firms over 20 years, for the years 1935--1954.

Usage

data("Grunfeld")

source

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

Details

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).

References

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 Unpublished Ph.D. Dissertation, University of Chicago.

See Also

Baltagi2002, Greene2003

Examples

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")