The goal of etwfe is to estimate extended (Mundlak) two-way fixed
effects a la Wooldridge
(2021).
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.
This package aims to simplify the process by providing convenience
functions that do the work for you. etwfe thus provides an R
equivalent of the
JWDID Stata
module and, indeed, shares some of the core design elements (albeit with
some internal differences).
While I’ve tested ewtfe against common use cases, please note that the package is still under early development and should be considered experimental. I plan (hope) to add some more features and the documentation could also be improved. You can help by identifying any bugs and filing issues.
You can install the development version of etwfe from GitHub:
# install.packages("remotes")
remotes::install_github("grantmcdermott/etwfe")Note: etwfe relies on the current development versions of fixest and marginaleffects. So you’ll have to compile fixest’s C++ source code on your system during installation.1 Once these dependencies make their way to CRAN, I’ll submit etwfe to CRAN as well so that binaries are available for easy install.
To demonstrate the core functionality of etwfe, we’ll use the
mpdta dataset from the did package. (You’ll need to install the
latter separately.)
# 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 1Now let’s see a simple example.2
library(etwfe)
mod =
etwfe(
fml = lemp ~ lpop, # outcome ~ controls
tvar = year, # time variable
gvar = first.treat, # group variable (with bespoke ref. level)
data = mpdta, # dataset
vcov = ~countyreal # vcov adjustment (here: clustered)
)
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
#> ... 6 coefficients remaining (display them with summary() or use argument n)
#> ... 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-4As you can see, the key etwfe() function is effectively a wrapper
around fixest::feols(). (Nonlinear models are also supported via the
family argument.) The resulting object is thus fully compatible with
other fixest methods and functions like etable().
fixest::etable(mod, signif.code = NA)
#> mod
#> Dependent Var.: lemp
#>
#> .Dtreat x first.treat = 2004 x year = 2004 -0.0213 (0.0217)
#> .Dtreat x first.treat = 2004 x year = 2005 -0.0819 (0.0274)
#> .Dtreat x first.treat = 2004 x year = 2006 -0.1379 (0.0308)
#> .Dtreat x first.treat = 2004 x year = 2007 -0.1095 (0.0323)
#> .Dtreat x first.treat = 2006 x year = 2006 0.0025 (0.0189)
#> .Dtreat x first.treat = 2006 x year = 2007 -0.0451 (0.0220)
#> .Dtreat x first.treat = 2007 x year = 2007 -0.0459 (0.0180)
#> .Dtreat x lpop_dm x first.treat = 2004 x year = 2004 0.0046 (0.0176)
#> .Dtreat x lpop_dm x first.treat = 2004 x year = 2005 0.0251 (0.0179)
#> .Dtreat x lpop_dm x first.treat = 2004 x year = 2006 0.0507 (0.0211)
#> .Dtreat x lpop_dm x first.treat = 2004 x year = 2007 0.0112 (0.0266)
#> .Dtreat x lpop_dm x first.treat = 2006 x year = 2006 0.0389 (0.0165)
#> .Dtreat x lpop_dm x first.treat = 2006 x year = 2007 0.0381 (0.0225)
#> .Dtreat x lpop_dm x first.treat = 2007 x year = 2007 -0.0198 (0.0162)
#> Fixed-Effects: ----------------
#> first.treat Yes
#> year Yes
#> Varying Slopes: ----------------
#> lpop (first.treat) Yes
#> lpop (year) Yes
#> ________________________________________ ________________
#> S.E.: Clustered by: countyreal
#> Observations 2,500
#> R2 0.87321
#> Within R2 0.00084While everyone likes a nice regression table, the raw coefficients from
an etwfe() estimation are not necessarily meaningful in of themselves.
Instead, we probably want to aggregate them along some dimension of
interest (e.g., an event study). A natural way to perform these
aggregations is by calculating marginal effects. The etwfe package
provides another convenience function for doing this, emfx(), which is
itself a thin(ish) wrapper around marginaleffects::marginaleffects().
# Other type options incl. "simple" (default), "calendar", and "group"
emfx(mod, type = "event")
#> Term Contrast event Effect Std. Error z value Pr(>|z|) 2.5 % 97.5 %
#> 1 .Dtreat mean(dY/dX) 0 -0.03321 0.01337 -2.484 0.01297951 -0.05941 -0.00701
#> 2 .Dtreat mean(dY/dX) 1 -0.05735 0.01715 -3.343 0.00082830 -0.09097 -0.02373
#> 3 .Dtreat mean(dY/dX) 2 -0.13787 0.03079 -4.477 7.5665e-06 -0.19823 -0.07751
#> 4 .Dtreat mean(dY/dX) 3 -0.10954 0.03232 -3.389 0.00070142 -0.17289 -0.04619
#>
#> Model type: etwfe
#> Prediction type: response- Jeffrey Wooldridge for the underlying theory behind ETWFE.
- Laurent Bergé (fixest) and Vincent Arel-Bundock (marginaleffects) for maintaining the two wonderful R packages that do most of the heavy lifting under the hood here.
- Fernando Rios-Avila for the
JWDIDStata module, which has provided a welcome foil for unit testing and whose elegant design helped inform my own choices for this R equivalent.
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