I am trying to estimate the model with 3 fixed effects. One is a customer-fixed effect, another one is good fixed effect and the third one is time-fixed effect. I am new to plm package, but as I understand, if I had just 2 fixed effects (time and good). I would do something like this:
fe <- plm(outcome ~ dependent variable + explanatory variable 1 + explanatory variable 2,
data = mydata, index = c("good_id", "time"), model = 'within', effect = "twoways")
But how I approach this problem in plm package if I have not 2 fixed effects, but 3?
You may add the third fixed effect as a dummy variable using factor(). Example:
library(plm)
data("Produc", package="plm")
# plm FE model
zz1 <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp +
factor(region),
data=Produc, index=c("state","year"), model='within', effect="twoways")
# LSDV model
zz2 <- lm(log(gsp) ~ 0 + log(pcap) + log(pc) + log(emp) + unemp
+ factor(state) + factor(year) + factor(region),
data=Produc)
summary(zz1)$coe
# Estimate Std. Error t-value Pr(>|t|)
# log(pcap) -0.030176057 0.026936544 -1.120265 2.629606e-01
# log(pc) 0.168828035 0.027656339 6.104497 1.655450e-09
# log(emp) 0.769306196 0.028141794 27.336786 1.275556e-114
# unemp -0.004221093 0.001138837 -3.706493 2.256597e-04
summary(zz2)$coe[1:4,]
# Estimate Std. Error t value Pr(>|t|)
# log(pcap) -0.030176057 0.026936544 -1.120265 2.629606e-01
# log(pc) 0.168828035 0.027656339 6.104497 1.655450e-09
# log(emp) 0.769306196 0.028141794 27.336786 1.275556e-114
# unemp -0.004221093 0.001138837 -3.706493 2.256597e-04
Yielding identical coefficients and statistics.