The goal of did2s is to estimate TWFE models without running into the problem caused by staggered treatment adoption. For details on the methodology, view this vignette
You can install did2s from github with:
net install did2s, replace from("https://raw.githubusercontent.com/kylebutts/did2s_stata/main/ado/")
* ssc install did2sI 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) second_stage(varlist) treatment(varname) cluster(varname)
first_stage: formula for first stage, can include fixed effects and covariates, but do not include treatment variable(s)!second_stage: List of treatment variables. This could be, for example a 0/1 treatment dummy, a set of event-study leads/lags, or a continuous treatment variabletreatment: 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
predict adj, residuals
reg adj i.treat, cluster(state) nocons
Linear regression Number of obs = 31,000
F(1, 39) = 2803.10
Prob > F = 0.0000
R-squared = 0.3776
Root MSE = 1.7505
(Std. Err. adjusted for 40 clusters in state)
------------------------------------------------------------------------------
| Robust
adj | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.treat | 2.380156 .0449558 52.94 0.000 2.289224 2.471087
------------------------------------------------------------------------------* With did2s standard error correction
did2s dep_var, first_stage(i.state i.year) second_stage(i.treat) treatment(treat) cluster(state)
(0 observations deleted)
(Std. Err. adjusted for clustering on state)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.treat | 2.380156 .0614383 38.74 0.000 2.259739 2.500573
------------------------------------------------------------------------------You can also do event-study by changing the second_stage
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) second_stage(ib100.rel_year_shift) treatment(treat) cluster(state)
(11,408 missing values generated)
(11,408 real changes made)
(0 observations deleted)
(Std. Err. adjusted for clustering on state)
--------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
rel_year_shift |
0 | .049467 .0795074 0.62 0.534 -.1063647 .2052986
1 | .1550051 .0793407 1.95 0.051 -.0004999 .31051
2 | .0429258 .0861871 0.50 0.618 -.1259978 .2118494
3 | .0798003 .0804802 0.99 0.321 -.0779379 .2375386
4 | .1023325 .0882446 1.16 0.246 -.0706237 .2752886
5 | .2164395 .0947508 2.28 0.022 .0307313 .4021477
6 | .1707938 .083863 2.04 0.042 .0064253 .3351622
7 | .0939678 .0805816 1.17 0.244 -.0639692 .2519048
8 | .089857 .0839759 1.07 0.285 -.0747327 .2544467
9 | .1976289 .0799969 2.47 0.013 .0408378 .35442
10 | .0952583 .0619518 1.54 0.124 -.0261651 .2166817
11 | .0512636 .0586073 0.87 0.382 -.0636046 .1661318
12 | .0877603 .0403517 2.17 0.030 .0086725 .1668482
13 | .1542402 .0439602 3.51 0.000 .0680799 .2404006
14 | .0220904 .0509833 0.43 0.665 -.0778349 .1220158
15 | .035128 .0489484 0.72 0.473 -.0608091 .1310651
16 | -.0508368 .0504833 -1.01 0.314 -.1497822 .0481087
17 | -.0094032 .0495642 -0.19 0.850 -.1065472 .0877408
18 | .0088808 .0564702 0.16 0.875 -.1017987 .1195602
19 | .1179048 .0515519 2.29 0.022 .016865 .2189447
20 | 1.726992 .0826976 20.88 0.000 1.564907 1.889076
21 | 1.752138 .0798351 21.95 0.000 1.595664 1.908612
22 | 1.871223 .0929743 20.13 0.000 1.688997 2.053449
23 | 1.918305 .0755331 25.40 0.000 1.770263 2.066347
24 | 1.939803 .0841477 23.05 0.000 1.774876 2.104729
25 | 2.145797 .0846879 25.34 0.000 1.979812 2.311782
26 | 2.180307 .0920339 23.69 0.000 1.999923 2.36069
27 | 2.347555 .0818049 28.70 0.000 2.18722 2.507889
28 | 2.412952 .0764437 31.57 0.000 2.263125 2.562779
29 | 2.619597 .1075448 24.36 0.000 2.408813 2.830381
30 | 2.680793 .0954052 28.10 0.000 2.493802 2.867784
31 | 2.712427 .120355 22.54 0.000 2.476536 2.948319
32 | 2.671961 .1533243 17.43 0.000 2.371451 2.972471
33 | 2.65589 .1224654 21.69 0.000 2.415862 2.895917
34 | 2.754846 .1293217 21.30 0.000 2.50138 3.008312
35 | 2.823183 .1341382 21.05 0.000 2.560277 3.086089
36 | 2.694037 .1199969 22.45 0.000 2.458847 2.929226
37 | 2.896575 .1265512 22.89 0.000 2.648539 3.14461
38 | 3.130081 .1160177 26.98 0.000 2.902691 3.357472
39 | 3.23066 .1235224 26.15 0.000 2.98856 3.472759
40 | 3.308015 .1120092 29.53 0.000 3.088481 3.527549
--------------------------------------------------------------------------------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) second_stage(i.post) treatment(post) cluster(sid)
* Covariates
did2s l_homicide [aweight=popwt], first_stage(i.sid i.year $demo) second_stage(i.post) treatment(post) cluster(sid)
(0 observations deleted)
(Std. Err. adjusted for clustering on sid)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.post | .0751416 .0353795 2.12 0.034 .0057991 .1444842
------------------------------------------------------------------------------
(0 observations deleted)
(Std. Err. adjusted for clustering on sid)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.post | .0760161 .0324715 2.34 0.019 .0123731 .1396591
------------------------------------------------------------------------------There are some situations where standard errors can not be calculate analytically in memory. The reason for this is that the analytic standard errors require the creation of the matrix containing all the fixed effects used in estimation. When there are a lot of observations and/or many fixed effects, this matrix can’t be stored in memory.
In this case, it’s possible to obtain standard errors via bootstrapping a custom program. Here is an example for the example data. You could spend time to make the command more programmable with args, but I find it easier to just write the estimation out.
use data/df_het.dta, clear
egen unique_id = group(state unit)
capture program drop did2s_est
program did2s_est, rclass
version 13.0
regress dep_var i.new_id i.year if treat == 0
tempvar dep_var_resid
predict `dep_var_resid', residuals
regress `dep_var_resid' ib0.treat, nocons
end
xtset unique_id year
sort unique_id year
bootstrap, cluster(state) idcluster(new_id) group(unique_id) reps(100): did2s_est
panel variable: unique_id (strongly balanced)
time variable: year, 1990 to 2020
delta: 1 unit
(running did2s_est on estimation sample)
Bootstrap replications (100)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
.................................................. 100
Linear regression Number of obs = 31,000
Replications = 100
Wald chi2(1) = 1568.60
Prob > chi2 = 0.0000
R-squared = 0.3776
Adj R-squared = 0.3776
Root MSE = 1.7505
(Replications based on 40 clusters in state)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
__000001 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.treat | 2.380156 .0600965 39.61 0.000 2.262369 2.497943
------------------------------------------------------------------------------Gardner, John. 2021. “Two-Stage Difference-in-Differences.” Working Paper. https://jrgcmu.github.io/2sdd_current.pdf.