eventstudyinteract (Sun and Abraham 2020)
Table of contents
Introduction
The eventstudyinteract command is written by Liyang Sun based on the Sun and Abraham 2020 paper Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.
The command potentially has some issues. See the code and the graphs below for details. If you have some insights on how to fix this, then please email me, or post in the issues section on GitHub.
Installation and options
Install the command from SSC:
ssc install eventstudyinteract, replace
Take a look at the help file:
help eventstudyinteract
Test the command
Please make sure that you generate the data using the script given here
Let’s try the basic eventstudyinteract command the never_treated as the control_cohort:
eventstudyinteract Y L_* F_*, vce(cluster id) absorb(id t) cohort(first_treat) control_cohort(never_treat)
which will show this output:
IW estimates for dynamic effects Number of obs = 1,800
Absorbing 2 HDFE groups F(236, 29) = .
Prob > F = .
R-squared = 0.9999
Adj R-squared = 0.9999
Root MSE = 1.0114
(Std. err. adjusted for 30 clusters in id)
------------------------------------------------------------------------------
| Robust
Y | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
L_0 | -.0705566 .4520534 -0.16 0.877 -.9951096 .8539963
L_1 | 8.477542 .4379319 19.36 0.000 7.581871 9.373214
L_2 | 17.69445 .6109211 28.96 0.000 16.44497 18.94392
L_3 | 25.97508 .6262269 41.48 0.000 24.6943 27.25585
L_4 | 34.74591 .9050206 38.39 0.000 32.89493 36.59688
L_5 | 42.5053 1.223893 34.73 0.000 40.00216 45.00845
L_6 | 51.87653 1.498852 34.61 0.000 48.81103 54.94202
L_7 | 60.49124 1.799291 33.62 0.000 56.81128 64.1712
L_8 | 69.1176 1.982205 34.87 0.000 65.06353 73.17167
L_9 | 77.28842 2.229958 34.66 0.000 72.72764 81.8492
L_10 | 85.61498 2.555366 33.50 0.000 80.38867 90.84129
L_11 | 93.99587 2.708476 34.70 0.000 88.45642 99.53533
L_12 | 103.5575 3.047215 33.98 0.000 97.32524 109.7897
L_13 | 110.9889 3.357933 33.05 0.000 104.1212 117.8567
L_14 | 119.482 3.757873 31.80 0.000 111.7963 127.1677
L_15 | 128.809 3.858744 33.38 0.000 120.9169 136.701
L_16 | 137.5689 4.21657 32.63 0.000 128.9451 146.1928
L_17 | 146.0436 4.597805 31.76 0.000 136.64 155.4471
L_18 | 154.8424 4.586774 33.76 0.000 145.4614 164.2234
L_19 | 163.6468 4.861225 33.66 0.000 153.7044 173.5891
L_20 | 171.9099 5.064144 33.95 0.000 161.5526 182.2673
L_21 | 179.6032 5.309208 33.83 0.000 168.7447 190.4618
L_22 | 189.3146 5.658889 33.45 0.000 177.7409 200.8883
L_23 | 203.2878 5.643981 36.02 0.000 191.7445 214.831
L_24 | 212.1516 5.841365 36.32 0.000 200.2046 224.0985
L_25 | 221.2972 6.233885 35.50 0.000 208.5475 234.0469
L_26 | 230.77 6.181821 37.33 0.000 218.1268 243.4132
L_27 | 268.352 .9300789 288.53 0.000 266.4497 270.2542
L_28 | 279.7236 .8413134 332.48 0.000 278.0029 281.4442
L_29 | 289.9827 .7938728 365.28 0.000 288.359 291.6063
L_30 | 299.9447 1.061719 282.51 0.000 297.7733 302.1162
L_31 | 308.1538 .7205883 427.64 0.000 306.68 309.6276
L_32 | 320.2466 .6393276 500.91 0.000 318.939 321.5542
L_33 | 329.467 .7404276 444.97 0.000 327.9527 330.9814
L_34 | 340.2075 .8140191 417.94 0.000 338.5426 341.8723
L_35 | 349.2469 .6694048 521.73 0.000 347.8778 350.6159
L_36 | 359.3307 .8590308 418.30 0.000 357.5737 361.0876
F_2 | .2679339 .5145674 0.52 0.607 -.7844745 1.320342
F_3 | .1303887 .3615731 0.36 0.721 -.6091113 .8698886
F_4 | -.1227137 .4505851 -0.27 0.787 -1.044264 .7988363
F_5 | -.3586946 .483613 -0.74 0.464 -1.347794 .6304051
F_6 | -.4161922 .4326221 -0.96 0.344 -1.301004 .4686194
F_7 | -.3196142 .4544008 -0.70 0.487 -1.248968 .6097397
F_8 | -.0626867 .4604898 -0.14 0.893 -1.004494 .8791206
F_9 | -.0926511 .4296329 -0.22 0.831 -.9713491 .7860469
F_10 | .1102947 .4471147 0.25 0.807 -.8041576 1.024747
F_11 | -.2264116 .4586641 -0.49 0.625 -1.164485 .7116618
F_12 | -.1182709 .519532 -0.23 0.822 -1.180833 .9442913
F_13 | .1261863 .5312998 0.24 0.814 -.9604437 1.212816
F_14 | .1467978 .3339677 0.44 0.664 -.5362428 .8298383
F_15 | .286046 .5164297 0.55 0.584 -.7701712 1.342263
F_16 | .1889184 .56528 0.33 0.741 -.9672089 1.345046
F_17 | -.7257636 .3417224 -2.12 0.042 -1.424664 -.0268629
F_18 | -.0111467 .4849795 -0.02 0.982 -1.003041 .9807478
F_19 | -.4036042 .3890966 -1.04 0.308 -1.199396 .3921876
F_20 | .083998 .3974126 0.21 0.834 -.7288021 .896798
F_21 | .0450013 .3697536 0.12 0.904 -.7112297 .8012323
F_22 | -.5576153 .4764867 -1.17 0.251 -1.53214 .4169094
F_23 | .1384066 .4293158 0.32 0.749 -.7396429 1.016456
F_24 | .9187571 .5323275 1.73 0.095 -.1699749 2.007489
F_25 | -.1651529 .5467614 -0.30 0.765 -1.283406 .9530998
F_26 | .0420085 .4311221 0.10 0.923 -.8397351 .9237522
F_27 | -.2333004 .5322703 -0.44 0.664 -1.321915 .8553146
F_28 | -.2905799 .4986613 -0.58 0.565 -1.310457 .729297
F_29 | .524472 .5439824 0.96 0.343 -.5880969 1.637041
F_30 | .4916469 .596472 0.82 0.417 -.7282752 1.711569
F_31 | .1394772 .5753502 0.24 0.810 -1.037246 1.3162
F_32 | .2195107 .4553865 0.48 0.633 -.7118592 1.150881
F_33 | .433028 .5843814 0.74 0.465 -.7621663 1.628222
F_34 | .0184928 .8632068 0.02 0.983 -1.746963 1.783949
F_35 | .106708 .5945981 0.18 0.859 -1.109382 1.322798
F_36 | .1068798 .7019857 0.15 0.880 -1.328842 1.542602
F_37 | .0648712 .6159508 0.11 0.917 -1.19489 1.324632
F_38 | -.8113278 1.236346 -0.66 0.517 -3.33994 1.717284
F_39 | -.5387131 .9568228 -0.56 0.578 -2.495636 1.418209
F_40 | -.7248558 1.007424 -0.72 0.478 -2.78527 1.335558
F_41 | -.2235736 .5398104 -0.41 0.682 -1.32761 .8804625
F_42 | -.3152135 1.014359 -0.31 0.758 -2.389811 1.759384
F_43 | .5507069 .8185869 0.67 0.506 -1.123491 2.224905
F_44 | .4711243 .7455523 0.63 0.532 -1.053701 1.99595
F_45 | -1.302809 .9750776 -1.34 0.192 -3.297066 .6914491
F_46 | -.1432933 .5311437 -0.27 0.789 -1.229604 .9430176
F_47 | -.5297326 1.077463 -0.49 0.627 -2.733392 1.673927
F_48 | -.3128089 .743813 -0.42 0.677 -1.834077 1.208459
F_49 | -.0590544 .7666954 -0.08 0.939 -1.627123 1.509014
F_50 | -1.156447 .3122062 -3.70 0.001 -1.79498 -.5179134
F_51 | -.2063065 .7599222 -0.27 0.788 -1.760522 1.347909
F_52 | .0657318 .5711765 0.12 0.909 -1.102455 1.233919
F_53 | -.5740659 .5549477 -1.03 0.309 -1.709061 .5609296
F_54 | .3670635 1.075711 0.34 0.735 -1.833013 2.56714
F_55 | -.4353981 .7411865 -0.59 0.561 -1.951295 1.080498
------------------------------------------------------------------------------
In order to plot the estimates we can use the event_plot (ssc install event_plot, replace) command where we restrict the figure to 10 leads and lags:
event_plot e(b_iw)#e(V_iw), default_look graph_opt(xtitle("Periods since the event") ytitle("Average effect") xlabel(-10(1)10) ///
title("eventstudyinteract")) stub_lag(L_#) stub_lead(F_#) trimlag(10) trimlead(10) together
And we get:
