model 在 R 中的 plm package 中具有三个固定效果

Question

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?

Answer 1

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.

Answer 2

Thanks to everyone for help: I actually found another solution myself which may add to provided ideas: https://cran.r-project.org/web/packages/fixest/vignettes/fixest_walkthrough.html#1_simple_example_using_trade_data This package allows inclusion of multiple fixed effects