did2s (Gardner 2021)
Table of contents
Introduction
The did2s command is written by Kyle Butts based on the Gardner 2021 paper Two-stage differences in differences. A detailed description is provided in this blog post.
Installation and options
ssc install did2s, replace
Take a look at the help file:
help did2s
Test the command
Let’s try the basic did2s command:
did2s Y, first_stage(id t) second_stage(F_* L_*) treatment(D) cluster(id)
which will show this output:
(Std. err. adjusted for clustering on id)
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
F_2 | .2253244 .2301779 0.98 0.328 -.225816 .6764647
F_3 | .128698 .2333503 0.55 0.581 -.3286603 .5860563
F_4 | -.1034735 .2210323 -0.47 0.640 -.5366889 .3297418
F_5 | .0552204 .1918748 0.29 0.774 -.3208473 .431288
F_6 | -.1979187 .2018475 -0.98 0.327 -.5935326 .1976951
F_7 | -.1802993 .2217714 -0.81 0.416 -.6149633 .2543646
F_8 | .0756883 .1691673 0.45 0.655 -.2558736 .4072502
F_9 | .0365711 .2123039 0.17 0.863 -.3795369 .452679
F_10 | .0605167 .1902589 0.32 0.750 -.312384 .4334174
L_0 | .0791996 .2780198 0.28 0.776 -.4657091 .6241084
L_1 | 8.619413 .330885 26.05 0.000 7.97089 9.267936
L_2 | 17.63192 .4278901 41.21 0.000 16.79327 18.47057
L_3 | 26.00454 .6601769 39.39 0.000 24.71061 27.29846
L_4 | 34.69155 .9373878 37.01 0.000 32.85431 36.5288
L_5 | 42.57469 1.320541 32.24 0.000 39.98648 45.16291
L_6 | 51.8403 1.579579 32.82 0.000 48.74439 54.93622
L_7 | 59.97723 1.914613 31.33 0.000 56.22466 63.7298
L_8 | 68.85919 2.106164 32.69 0.000 64.73118 72.98719
L_9 | 77.24698 2.323762 33.24 0.000 72.69249 81.80147
L_10 | 85.82518 2.649239 32.40 0.000 80.63277 91.01759
------------------------------------------------------------------------------
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") xlabel(-10(1)10) ///
title("did2s")) stub_lag(L_#) stub_lead(F_#) together
And we get:
