did_imputation (Borusyak, Jaravel, Spiess 2021)
Table of contents
Introduction
The did_imputation command is written by Kirill Borusyak, Xavier Jaravel, and Jann Spiess, based on their paper Revisiting Event Study Designs: Robust and Efficient Estimation.
Installation and options
ssc install did_imputation, replace
Take a look at the help file:
help did_imputation
Test the command
Please make sure that you generate the data using the script given here
Let’s try the basic did_imputation command with 10 leads and lags:
did_imputation Y i t first_treat, horizons(0/10) pretrend(10) minn(0)
which gives us:
Number of obs = 1,438
------------------------------------------------------------------------------
Y | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
tau0 | .0787163 .2677837 0.29 0.769 -.44613 .6035626
tau1 | 8.637227 .2815466 30.68 0.000 8.085406 9.189048
tau2 | 17.78728 .2329168 76.37 0.000 17.33078 18.24379
tau3 | 26.07725 .2293303 113.71 0.000 25.62777 26.52673
tau4 | 34.76562 .2815757 123.47 0.000 34.21374 35.3175
tau5 | 42.93274 .2848896 150.70 0.000 42.37437 43.49111
tau6 | 52.00695 .2787334 186.58 0.000 51.46064 52.55326
tau7 | 60.20919 .2526519 238.31 0.000 59.714 60.70438
tau8 | 68.90038 .2317813 297.26 0.000 68.4461 69.35466
tau9 | 77.38383 .2652313 291.76 0.000 76.86399 77.90368
tau10 | 85.83142 .309541 277.29 0.000 85.22473 86.43811
pre1 | .1206052 .2866341 0.42 0.674 -.4411872 .6823977
pre2 | .2116369 .3320804 0.64 0.524 -.4392287 .8625025
pre3 | .0601094 .2808867 0.21 0.831 -.4904184 .6106372
pre4 | .0568874 .2809316 0.20 0.840 -.4937285 .6075032
pre5 | -.2050823 .2642319 -0.78 0.438 -.7229672 .3128027
pre6 | -.3225205 .2343503 -1.38 0.169 -.7818387 .1367977
pre7 | -.2239894 .2511694 -0.89 0.373 -.7162724 .2682936
pre8 | .052194 .2243524 0.23 0.816 -.3875287 .4919166
pre9 | .0285485 .2358903 0.12 0.904 -.4337881 .490885
pre10 | .0962904 .2314293 0.42 0.677 -.3573028 .5498836
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In order to plot the estimates we can use the event_plot (ssc install event_plot, replace) command as follows:
event_plot, default_look graph_opt(xtitle("Periods since the event") ytitle("Average effect") ///
title("did_imputation") xlabel(-10(1)10)) stub_lag(tau#) stub_lead(pre#) together
And we get:
