# NOT RUN {
data("Produc", package = "plm")
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state","year"))
summary(zz)
# replicates some results from Baltagi (2013), table 3.1
data("Grunfeld", package = "plm")
p <- plm(inv ~ value + capital,
data = Grunfeld, model = "pooling")
wi <- plm(inv ~ value + capital,
data = Grunfeld, model = "within", effect = "twoways")
swar <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", effect = "twoways")
amemiya <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "amemiya",
effect = "twoways")
walhus <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "walhus",
effect = "twoways")
# summary and summary with a funished vcov (passed as matrix,
# as function, and as function with additional argument)
summary(wi)
summary(wi, vcov = vcovHC(wi))
summary(wi, vcov = vcovHC)
summary(wi, vcov = function(x) vcovHC(x, method = "white2"))
# nested random effect model
# replicate Baltagi/Song/Jung (2001), p. 378 (table 6), columns SA, WH
# == Baltagi (2013), pp. 204-205
data("Produc", package = "plm")
pProduc <- pdata.frame(Produc, index = c("state", "year", "region"))
form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp
summary(plm(form, data = pProduc, model = "random", effect = "nested"))
summary(plm(form, data = pProduc, model = "random", effect = "nested",
random.method = "walhus"))
## Hausman-Taylor estimator and Amemiya-MaCurdy estimator
## replicate Baltagi (2005, 2013), table 7.4
data("Wages", package = "plm")
ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) +
bluecol + ind + union + sex + black + ed |
bluecol + south + smsa + ind + sex + black |
wks + married + union + exp + I(exp ^ 2),
data = Wages, index = 595,
random.method = "ht", model = "random", inst.method = "baltagi")
summary(ht)
am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) +
bluecol + ind + union + sex + black + ed |
bluecol + south + smsa + ind + sex + black |
wks + married + union + exp + I(exp ^ 2),
data = Wages, index = 595,
random.method = "ht", model = "random", inst.method = "am")
summary(am)
# }
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