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, nocons
predict adj, residuals
reg adj i.treat, cluster(state) nocons
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
------------------------------------------------------------------------------* With did2s standard error correction
did2s dep_var, first_stage(i.state i.year) second_stage(i.treat) treatment(treat) cluster(state)
(Std. Err. adjusted for clustering on state)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.treat | 2.380156 .0614477 38.73 0.000 2.25972 2.500591
------------------------------------------------------------------------------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)
(Std. Err. adjusted for clustering on state)
--------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
rel_year_shift |
0 | .049467 .0780278 0.63 0.526 -.1034647 .2023986
1 | .1550051 .0793378 1.95 0.051 -.0004941 .3105043
2 | .0429258 .0861951 0.50 0.618 -.1260136 .2118651
3 | .0798003 .0804722 0.99 0.321 -.0779223 .237523
4 | .1023325 .0882875 1.16 0.246 -.0707078 .2753728
5 | .2164395 .0947135 2.29 0.022 .0308044 .4020746
6 | .1707938 .0838902 2.04 0.042 .0063719 .3352156
7 | .0939678 .0806006 1.17 0.244 -.0640064 .251942
8 | .089857 .0839978 1.07 0.285 -.0747756 .2544897
9 | .1976289 .0799682 2.47 0.013 .0408941 .3543637
10 | .0952583 .0640277 1.49 0.137 -.0302337 .2207503
11 | .0512636 .0586116 0.87 0.382 -.063613 .1661402
12 | .0877603 .0403556 2.17 0.030 .0086649 .1668558
13 | .1542402 .0439649 3.51 0.000 .0680706 .2404099
14 | .0220904 .0509844 0.43 0.665 -.0778372 .1220181
15 | .035128 .0489496 0.72 0.473 -.0608115 .1310675
16 | -.0508368 .0504788 -1.01 0.314 -.1497735 .0481
17 | -.0094032 .049567 -0.19 0.850 -.1065527 .0877463
18 | .0088808 .0564685 0.16 0.875 -.1017955 .1195571
19 | .1179048 .0515501 2.29 0.022 .0168685 .2189412
20 | 1.726992 .0827157 20.88 0.000 1.564872 1.889111
21 | 1.752138 .0798513 21.94 0.000 1.595633 1.908644
22 | 1.871223 .0929891 20.12 0.000 1.688968 2.053478
23 | 1.918305 .0755381 25.40 0.000 1.770253 2.066357
24 | 1.939803 .0841515 23.05 0.000 1.774869 2.104737
25 | 2.145797 .084697 25.33 0.000 1.979794 2.3118
26 | 2.180307 .0920414 23.69 0.000 1.999909 2.360704
27 | 2.347555 .0818194 28.69 0.000 2.187192 2.507918
28 | 2.412952 .0764653 31.56 0.000 2.263083 2.562822
29 | 2.619597 .1075586 24.36 0.000 2.408786 2.830408
30 | 2.680793 .0954201 28.09 0.000 2.493773 2.867813
31 | 2.712427 .1203408 22.54 0.000 2.476564 2.948291
32 | 2.671961 .1533124 17.43 0.000 2.371475 2.972448
33 | 2.65589 .1224591 21.69 0.000 2.415874 2.895905
34 | 2.754846 .129317 21.30 0.000 2.50139 3.008303
35 | 2.823183 .1341359 21.05 0.000 2.560281 3.086084
36 | 2.694037 .1199964 22.45 0.000 2.458848 2.929225
37 | 2.896575 .126548 22.89 0.000 2.648545 3.144604
38 | 3.130081 .1160191 26.98 0.000 2.902688 3.357475
39 | 3.23066 .1235199 26.15 0.000 2.988565 3.472754
40 | 3.308015 .1120013 29.54 0.000 3.088496 3.527533
--------------------------------------------------------------------------------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)
(Std. Err. adjusted for clustering on sid)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.post | .0751416 .0572312 1.31 0.189 -.0370295 .1873128
------------------------------------------------------------------------------
(Std. Err. adjusted for clustering on sid)
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.post | .0760161 .0509904 1.49 0.136 -.0239232 .1759554
------------------------------------------------------------------------------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_hom.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
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) = 739.20
Prob > chi2 = 0.0000
R-squared = 0.2279
Adj R-squared = 0.2278
Root MSE = 1.7302
(Replications based on 40 clusters in state)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
__000001 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.treat | 1.98256 .0729197 27.19 0.000 1.83964 2.12548
_cons | -7.42e-11 4.03e-10 -0.18 0.854 -8.65e-10 7.16e-10
------------------------------------------------------------------------------Gardner, John. 2021. “Two-Stage Difference-in-Differences.” Working Paper. https://jrgcmu.github.io/2sdd_current.pdf.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent