[R] Fixed Effects Estimations (in Panel Data)

[ivo welch]

dear readers---I struggled with how to do nice fixed-effects
regressions in large economic samples for a while.  Eventually, I
realized that nlme is not really what I needed (too complex), and all
I really wanted is the plm package.  so, I thought I would share a
quick example.

################ sample code to show fixed-effects models  in R
# create a sample panel data set with firms and years

set.seed(0)

fm= as.factor( c(rep("A", 5),  rep("B", 5),  rep("C", 5),  rep("D", 5) ))
yr= as.factor( rep( c(1985,1986,1987,1988,1989), 4))
d= data.frame( fm, yr, y=rnorm(length(yr)), x=rnorm(length(yr)))

# first, the non-specific way.  slow.  lots of memory.  solid.  no
panel-data expertise needed

print(summary(lm( y ~ x + as.factor(fm) -1, data=d)))
print(summary(lm( y ~ x + as.factor(yr) -1, data=d)))
print(summary(lm( y ~ x + as.factor(yr) + as.factor(fm) -1, data=d)))

# second, the specific plm way.  fast. additional functionality

library(plm);  ## also, there is an an excellent  vignette("plm",
package = "plm")

pd= pdata.frame( d, index=c("fm", "yr") )  # perhaps try the
"drop.index=TRUE" argument and look at your output

print(summary(plm( y ~ x, data=pd, model="within", effect="individual"
)))  ### effect="individual" is the default --- this is firm-fixed
effects
print(summary(plm( y ~ x, data=pd, model="within", effect="time" )))
### this is year-fixed effects
print(summary(plm( y ~ x, data=pd, model="within", effect="twoways"
)))  ### this is both


(I have not yet verified that the plm regressions avoid computations
of the factors [i.e., that they do not build an X'X matrix that
includes the number of fixed effects, but work through averaging], but
I presume that they do.  this is of course useful for very large panel
data sets with many thousands of fixed effects.)

and, thanks, Yves and Giovanni for writing plm().

/iaw
----
Ivo Welch (ivo.welch at brown.edu, ivo.welch at gmail.com)