The goal of etwfe is to estimate extended two-way fixed effects a la Wooldridge (2021, 2022). Briefly, Wooldridge proposes a set of saturated interaction effects to overcome the potential bias problems of vanilla TWFE in difference-in-differences designs. The Wooldridge solution is intuitive and elegant, but rather tedious and error prone to code up manually. The etwfe package aims to simplify the process by providing convenience functions that do the work for you.
Documentation is available on the package homepage.
You can install etwfe from CRAN.
Or, you can grab the development version from R-universe.
A detailed walkthrough of etwfe is provided in the introductory vignette (available online, or by typing vignette("etwfe") in your R console). But here’s a quickstart example to demonstrate the basic syntax.
library(etwfe)
# install.packages("did")
data("mpdta", package = "did")
head(mpdta)
#> year countyreal lpop lemp first.treat treat
#> 866 2003 8001 5.896761 8.461469 2007 1
#> 841 2004 8001 5.896761 8.336870 2007 1
#> 842 2005 8001 5.896761 8.340217 2007 1
#> 819 2006 8001 5.896761 8.378161 2007 1
#> 827 2007 8001 5.896761 8.487352 2007 1
#> 937 2003 8019 2.232377 4.997212 2007 1
# Estimate the model
mod =
etwfe(
fml = lemp ~ lpop, # outcome ~ controls
tvar = year, # time variable
gvar = first.treat, # group variable
data = mpdta, # dataset
vcov = ~countyreal # vcov adjustment (here: clustered)
)
# This gives us a regression model with fully saturated interactions
mod
#> OLS estimation, Dep. Var.: lemp
#> Observations: 2,500
#> Fixed-effects: first.treat: 4, year: 5
#> Varying slopes: lpop (first.treat): 4, lpop (year): 5
#> Standard-errors: Clustered (countyreal)
#> Estimate Std. Error t value Pr(>|t|)
#> .Dtreat:first.treat::2004:year::2004 -0.021248 0.021728 -0.977890 3.2860e-01
#> .Dtreat:first.treat::2004:year::2005 -0.081850 0.027375 -2.989963 2.9279e-03 **
#> .Dtreat:first.treat::2004:year::2006 -0.137870 0.030795 -4.477097 9.3851e-06 ***
#> .Dtreat:first.treat::2004:year::2007 -0.109539 0.032322 -3.389024 7.5694e-04 ***
#> .Dtreat:first.treat::2006:year::2006 0.002537 0.018883 0.134344 8.9318e-01
#> .Dtreat:first.treat::2006:year::2007 -0.045093 0.021987 -2.050907 4.0798e-02 *
#> .Dtreat:first.treat::2007:year::2007 -0.045955 0.017975 -2.556568 1.0866e-02 *
#> .Dtreat:first.treat::2004:year::2004:lpop_dm 0.004628 0.017584 0.263184 7.9252e-01
#> .Dtreat:first.treat::2004:year::2005:lpop_dm 0.025113 0.017904 1.402661 1.6134e-01
#> .Dtreat:first.treat::2004:year::2006:lpop_dm 0.050735 0.021070 2.407884 1.6407e-02 *
#> .Dtreat:first.treat::2004:year::2007:lpop_dm 0.011250 0.026617 0.422648 6.7273e-01
#> .Dtreat:first.treat::2006:year::2006:lpop_dm 0.038935 0.016472 2.363731 1.8474e-02 *
#> .Dtreat:first.treat::2006:year::2007:lpop_dm 0.038060 0.022477 1.693276 9.1027e-02 .
#> .Dtreat:first.treat::2007:year::2007:lpop_dm -0.019835 0.016198 -1.224528 2.2133e-01
#> ... 10 variables were removed because of collinearity (.Dtreat:first.treat::2006:year::2004, .Dtreat:first.treat::2006:year::2005 and 8 others [full set in $collin.var])
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.537131 Adj. R2: 0.87167
#> Within R2: 8.449e-4
# Pass to emfx() to recover the ATTs of interest. Here's an event-study example.
emfx(mod, type = "event")
#>
#> Term Contrast event Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> .Dtreat mean(TRUE) - mean(FALSE) 0 -0.0332 0.0134 -2.48 0.013 6.3 -0.0594 -0.00701
#> .Dtreat mean(TRUE) - mean(FALSE) 1 -0.0573 0.0172 -3.34 <0.001 10.2 -0.0910 -0.02373
#> .Dtreat mean(TRUE) - mean(FALSE) 2 -0.1379 0.0308 -4.48 <0.001 17.0 -0.1982 -0.07751
#> .Dtreat mean(TRUE) - mean(FALSE) 3 -0.1095 0.0323 -3.39 <0.001 10.5 -0.1729 -0.04619
#>
#> Columns: term, contrast, event, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted
#> Type: responseJWDID Stata module, which has provided a welcome foil for unit testing and whose elegant design helped inform my own choices for this R equivalent.