The goal of lfe is to speed up the estimation of linear models with large fixed effects. It includes support for instrumental variables, conditional F statistics for weak instruments, robust and multi-way clustered standard errors, as well as limited mobility bias correction. See Gaure (2013) doi:10.1016/j.csda.2013.03.024 and Gaure 2014 doi:10.1002/sta4.68.
You can install the released version of lfe from CRAN with:
install.packages("lfe")You can install the development version of lfe like so:
remotes::install_github("MatthieuStigler/lfe")This is a basic example which shows you the speed improvement over base R for fixed effects estimation.
library(lfe) # fixed effects estimation
library(tradepolicy) # intl trade data
library(dplyr) # data cleaning/transforming
training_data <- agtpa_applications %>%
mutate(
log_trade = log(trade),
log_dist = log(dist),
exp_year = paste(exporter, year, sep = "_"),
imp_year = paste(importer, year, sep = "_")
) %>%
filter(trade > 0, exporter != importer, year %in% seq(1986, 2006, 4)) %>%
select(year, log_trade, log_dist, cntg, lang, clny, rta, exp_year, imp_year)
# note the difference with the | operator to indicate the FEs
# this is just an example, here I am not estimating a PPML model or anything
# in the state of the art
fml1 <- 0 + log_trade ~
log_dist + cntg + lang + clny + rta + exp_year + imp_year # base
fml2 <- log_trade ~
log_dist + cntg + lang + clny + rta | exp_year + imp_year # lfe
lm(fml1, data = training_data)
felm(fml2, data = training_data)For a complete test with devtools::check(), you need to
run sudo apt-get install devtools or similar before. The
package is written in C, in the future I shall try to rewrite it in C++
to ease long term maintenance.
The package also needs additional testing. At the present time,the tests it cover around 30% of the written lines.