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The goal of did2s is to estimate TWFE models without running into the problem of staggered treatment adoption. For details on the methodology, view this vignette
You can install did2s from github with:
net install did2s, from("https://raw.githubusercontent.com/kylebutts/did2s_stata/main/ado/")
* ssc install did2s
I have created an Stata package with the help of John Gardner to
estimate the two-stage procedure. The command is did2s which estimates
the two-stage did procedure. This function requires the following syntax
did2s depvar [if] [in] [weight], first_stage(varlist) treat_formula(varlist) treat_var(varname) cluster(varname)
first_stage: formula for first stage, can include fixed effects
and covariates, but do not include treatment variable(s)!treat_formula: Second stage, these should be the treatment
indicator(s) (e.g. treatment variable or es leads/lags), use i() for
factor variables, following fixest::feols.treat_var: This has to be the 0/1 treatment variable that marks
when treatment turns on for a unit. If you suspect anticipation, see
note above for accounting for this.cluster: Which variable to cluster on.To view the documentation, type help did2s into the console.
********************************************************************************
* Static
********************************************************************************
use data/df_het.dta
* Manually (note standard errors are off)
qui reg dep_var i.state i.year if treat == 0, nocons
predict adj, residuals
reg adj i.treat, cluster(state) nocons
* With did2s standard error correction
did2s dep_var, first_stage(i.state i.year) treat_formula(i.treat) treat_var(treat) cluster(state)
Linear regression Number of obs = 31,000
F(1, 39) = 2787.70
Prob > F = 0.0000
R-squared = 0.3776
Root MSE = 1.7506
(Std. Err. adjusted for 40 clusters in state)
------------------------------------------------------------------------------
| Robust
adj | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.treat | 2.380208 .0450809 52.80 0.000 2.289024 2.471393
------------------------------------------------------------------------------
(Std. Err. adjusted for clustering on state)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.treat | 2.380208 .0614314 38.75 0.000 2.259805 2.500612
------------------------------------------------------------------------------
You can also do event-study by changing the treat_formula
use data/df_het.dta
* can not have negatives in factor variable
gen rel_year_shift = rel_year + 20
replace rel_year_shift = 100 if rel_year_shift == .
did2s dep_var, first_stage(i.state i.year) treat_formula(ib100.rel_year_shift) treat_var(treat) cluster(state)
(11,408 missing values generated)
(11,408 real changes made)
(Std. Err. adjusted for clustering on state)
--------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
rel_year_shift |
0 | .0746601 .0839355 0.89 0.374 -.0898505 .2391707
1 | .155387 .0793007 1.96 0.050 -.0000395 .3108135
2 | .0433077 .0861965 0.50 0.615 -.1256343 .2122497
3 | .0801822 .0804671 1.00 0.319 -.0775303 .2378948
4 | .1027144 .088289 1.16 0.245 -.0703289 .2757576
5 | .2168214 .0947375 2.29 0.022 .0311394 .4025035
6 | .1711757 .0839522 2.04 0.041 .0066325 .3357189
7 | .0943497 .0806924 1.17 0.242 -.0638044 .2525039
8 | .0902389 .0839479 1.07 0.282 -.074296 .2547738
9 | .1980108 .0799579 2.48 0.013 .0412963 .3547253
10 | .1079317 .0650773 1.66 0.097 -.0196175 .235481
11 | .0512958 .0586111 0.88 0.381 -.0635799 .1661715
12 | .0877925 .0403538 2.18 0.030 .0087006 .1668845
13 | .1542725 .0439659 3.51 0.000 .0681009 .2404441
14 | .0221227 .0509763 0.43 0.664 -.077789 .1220343
15 | .0351602 .0489502 0.72 0.473 -.0607804 .1311009
16 | -.0508045 .0504791 -1.01 0.314 -.1497417 .0481326
17 | -.0093709 .049563 -0.19 0.850 -.1065126 .0877707
18 | .008913 .0564742 0.16 0.875 -.1017744 .1196004
19 | .1179371 .0515514 2.29 0.022 .0168982 .2189759
20 | 1.72709 .0827019 20.88 0.000 1.564997 1.889183
21 | 1.752237 .0798446 21.95 0.000 1.595744 1.908729
22 | 1.871322 .0929648 20.13 0.000 1.689114 2.053529
23 | 1.918404 .0755407 25.40 0.000 1.770347 2.066461
24 | 1.939901 .0841578 23.05 0.000 1.774955 2.104847
25 | 2.145896 .0846846 25.34 0.000 1.979917 2.311874
26 | 2.180405 .0920294 23.69 0.000 2.000031 2.360779
27 | 2.347653 .0818133 28.70 0.000 2.187302 2.508004
28 | 2.413051 .0764681 31.56 0.000 2.263176 2.562925
29 | 2.619696 .10755 24.36 0.000 2.408901 2.83049
30 | 2.681013 .0954122 28.10 0.000 2.494008 2.868017
31 | 2.712357 .1203332 22.54 0.000 2.476509 2.948206
32 | 2.671891 .1532795 17.43 0.000 2.371469 2.972314
33 | 2.65582 .1224423 21.69 0.000 2.415837 2.895802
34 | 2.754776 .1293034 21.30 0.000 2.501346 3.008206
35 | 2.823113 .1341072 21.05 0.000 2.560267 3.085958
36 | 2.693967 .1199888 22.45 0.000 2.458793 2.929141
37 | 2.896505 .1265275 22.89 0.000 2.648515 3.144494
38 | 3.130011 .1160092 26.98 0.000 2.902638 3.357385
39 | 3.23059 .1235021 26.16 0.000 2.98853 3.472649
40 | 3.307945 .1119849 29.54 0.000 3.088458 3.527431
--------------------------------------------------------------------------------
This method works with pre-determined covariates as well!
********************************************************************************
* Castle Doctrine
********************************************************************************
use https://github.com/scunning1975/mixtape/raw/master/castle.dta, clear
* Define Covariates
global demo blackm_15_24 whitem_15_24 blackm_25_44 whitem_25_44
* No Covariates
did2s l_homicide [aweight=popwt], first_stage(i.sid i.year) treat_formula(i.post) treat_var(post) cluster(sid)
* Covariates
did2s l_homicide [aweight=popwt], first_stage(i.sid i.year $demo) treat_formula(i.post) treat_var(post) cluster(sid)
(Std. Err. adjusted for clustering on sid)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.post | .1454483 .1012669 1.44 0.151 -.0530313 .3439279
------------------------------------------------------------------------------
(Std. Err. adjusted for clustering on sid)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.post | .0802279 .0540177 1.49 0.137 -.0256447 .1861006
------------------------------------------------------------------------------
Gardner, John. 2021. “Two-Stage Difference-in-Differences.” Working Paper. https://jrgcmu.github.io/2sdd_current.pdf.
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