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> ## ----------------------------------------------------------------------------
> ## Griliches and Wallace (1965) -> Grunfeld (1958)
> ##
> ## data
> ## availability: none
> ## firms: Chrysler, Goodyear, Atlantic Refining, Westinghouse, Union Oil, General Motors
> ## note: "Only six of the original eleven firms are used in this study, because we did not
> ##        succeed in reproducing and extending the original set of data for the others." (p. 313, fn. 8)
> ##
> ## analysis
> ## result: GM cannot be reproduced. (see also Grunfeld, 1958)
> ##         Small deviations for aggregate, possibly due to problems with GM.
> ##         G&W construct alternative model based on further variables and
> ##         evaluate models in 1955-1960 post-sample period
> ## ----------------------------------------------------------------------------
> 
> ## preliminaries
> source("start.R")
Loading required package: kinship
Loading required package: survival
Loading required package: splines
Loading required package: nlme
Loading required package: lattice
[1] "kinship is loaded"
Loading required package: Formula
Loading required package: MASS
Loading required package: sandwich
Loading required package: zoo
Loading required package: Matrix
Loading required package: car
Loading required package: lmtest
> 
> ## data pre-processing
> gr <- subset(Grunfeld, firm %in% c("Chrysler", "Goodyear", "Atlantic Refining",
+   "Westinghouse", "Union Oil", "General Motors"))
> gr$firm <- factor(gr$firm)
> pgr <- plm.data(gr, c("firm", "year"))
> 
> ## Grunfeld model (Table 1, p. 314)
> ## p. 313: "Table 1 reproduces Grunfeld's original estimates"
> sf <- systemfit(invest ~ value + capital, method = "OLS", data = pgr)
> gsummary(sf, sigma2 = FALSE)

Method: OLS 
                  (Intercept) value capital   R^2
Atlantic.Refining      22.707 0.162   0.003 0.680
                        6.872 0.057   0.022      
Chrysler               -6.190 0.078   0.316 0.914
                       13.506 0.020   0.029      
General.Motors       -149.782 0.119   0.371 0.921
                      105.842 0.026   0.037      
Goodyear               -7.723 0.075   0.082 0.666
                        9.359 0.034   0.028      
Union.Oil              -4.500 0.088   0.124 0.764
                       11.289 0.066   0.017      
Westinghouse           -0.509 0.053   0.092 0.744
                        8.015 0.016   0.056      
> 
> ## alternatively:
> lm_CH <- lm(invest ~ capital + value, data = gr, subset = firm == "Chrysler")
> lm_GY <- lm(invest ~ capital + value, data = gr, subset = firm == "Goodyear")
> lm_AR <- lm(invest ~ capital + value, data = gr, subset = firm == "Atlantic Refining")
> lm_WH <- lm(invest ~ capital + value, data = gr, subset = firm == "Westinghouse")
> lm_UO <- lm(invest ~ capital + value, data = gr, subset = firm == "Union Oil")
> lm_GM <- lm(invest ~ capital + value, data = gr, subset = firm == "General Motors")
> 
> ## aggregate regression
> gr_macro <- aggregate(gr[,-4], list(gr$year), sum)[,-1]
> lm_macro <- lm(invest ~ capital + value, data = gr_macro)
> gsummary(lm_macro, sigma2 = FALSE)
 (Intercept) value capital   R^2
    -312.405 0.242   0.113 0.946
     113.128 0.023   0.020      
>