eventstudyinteract
Table of contents
Notes
- Based on: Sun and Abraham (2020). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects
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Program version (if available): 0.5
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Last checked: 7 Jul 2026
- Additional info in this blog post.
Installation and options
ssc install eventstudyinteract, replace
Take a look at the help file:
help eventstudyinteract
Test the command
Please make sure that you generate the shared setup data using the setup block 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:
(obs=1,380)
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 = 1.0111
(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:
