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.we...@brown.edu, ivo.we...@gmail.com)
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