Extended Two-way Fixed Effects (ETWFE)

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The goal of etwfe is to estimate extended two-way fixed effects a la Wooldridge (2021, 2023). 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.

Installation

You can install etwfe from CRAN.

install.packages("etwfe")

Or, you can grab the development version from R-universe.

install.packages("etwfe", repos = "https://grantmcdermott.r-universe.dev")

Quickstart example

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.

Start by loading the package and some data.

library(etwfe)

# install.packages("did")
data("mpdta", package = "did")
head(mpdta, 2)
#>     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

Step 1: Run etwfe() to estimate a model with full saturated interactions.

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)
)
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

Step 2: Pass to emfx() to recover the ATTs of interest. In this case, an event-study example.

emfx(mod, type = "event")
#> 
#>     Term event Estimate Std. Error     z Pr(>|z|)    S   2.5 %   97.5 %
#>  .Dtreat     0  -0.0332     0.0134 -2.48    0.013  6.3 -0.0594 -0.00701
#>  .Dtreat     1  -0.0573     0.0172 -3.34   <0.001 10.2 -0.0910 -0.02373
#>  .Dtreat     2  -0.1379     0.0308 -4.48   <0.001 17.0 -0.1982 -0.07751
#>  .Dtreat     3  -0.1095     0.0323 -3.39   <0.001 10.5 -0.1729 -0.04619
#> 
#> Type:  response 
#> Comparison: TRUE - FALSE
#> Columns: term, contrast, event, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high

Acknowledgements