One example comparing all

[azev77]

Hi and thank you for this! I hacked into your code to create one example that applies all estimators on the same dataset, creating the dataset only once. This can be super-useful!

/* Install*/
if 1==1{
ssc install ftools, replace
ssc install reghdfe, replace
ssc install event_plot, replace

/* 0: TWFE */
xtreg Y D t, fe             /*   */
xtreg Y D i.t, fe           /*   */
reghdfe Y D, absorb(id t)   /*   */     
reg Y D i.t i.id            /*   */

/* 0: bacondecomp */
ssc install bacondecomp
bacondecomp Y D, ddetail
ereturn list
display e(dd_avg_e)*e(wt_sum_e) + e(dd_avg_l)*e(wt_sum_l) + e(dd_avg_u)*e(wt_sum_u)

/* 1: did_multiplegt */
ssc install did_multiplegt, replace
help did_multiplegt
did_multiplegt Y G T D

/* 2: csdid */
ssc install csdid, replace
ssc install drdid, replace

help csdid
csdid Y [ind vars], [ivar(varname)] time(varname) gvar(varname) [options]
csdid Y, ivar(id) time(t) gvar(gvar) notyet
estat event, window(-10 10) estore(cs) 

event_plot cs, default_look graph_opt(xtitle("t") ytitle("ATT") ///
    title("csdid") xlabel(-10(1)10)) stub_lag(Tp#) stub_lead(Tm#) together   

/* 3: did_imputation */
ssc install did_imputation, replace
help did_imputation
did_imputation Y i t first_treat

/* 4: eventstudyinteract (Sun and Abraham 2020) */
*ssc install eventstudyinteract, replace

net install github, from("https://haghish.github.io/github/")
github install lsun20/eventstudyinteract
ssc install eventstudyweights , replace

help eventstudyinteract
eventstudyinteract Y *lags* *leads*, vce(cluster *var*) absorb(*i* *t*) cohort(first_treat) control_cohort(*variable*)

/* 5: stackedev (Cengiz, Dube, Lindner, Zipperer 2019) */
ssc install stackedev, replace
help stackedev
stackedev Y F* L* , cohort(first_treat) time(t) never_treat(no_treat) unit_fe(i) clust_unit(i)

/* 6: did2s (Gardner 2021) */
ssc install did2s, replace
help did2s
did2s Y, first_stage(i t) second_stage(*leads* *lags*) treat_var(*D*) cluster(*var*)

}

/* MAKE DATA: id t Y D cohort effect first_treat rel_time  */
if 1==1{
clear

local units = 30
local start = 1
local end   = 60

local time = `end' - `start' + 1
local obsv = `units' * `time'
set obs `obsv'

egen id    = seq(), b(`time')  
egen t     = seq(), f(`start') t(`end')     

sort  id t
xtset id t

set seed 20211222

gen Y           = 0     // outcome variable 
gen D           = 0     // intervention variable
gen cohort      = .     // treatment cohort
gen effect      = .     // treatment effect size
gen first_treat = .     // when the treatment happens for each cohort
gen rel_time    = .     // time - first_treat

levelsof id, local(lvls)
foreach x of local lvls {
    local chrt = runiformint(0,5)   
    replace cohort = `chrt' if id==`x'
}

levelsof cohort , local(lvls)  
foreach x of local lvls {

    local eff = runiformint(2,10)
        replace effect = `eff' if cohort==`x'

    local timing = runiformint(`start',`end' + 20)  // 
    replace first_treat = `timing' if cohort==`x'
    replace first_treat = . if first_treat > `end'
        replace D = 1 if cohort==`x' & t>= `timing' 
}

replace rel_time = t - first_treat
replace Y = id + t + cond(D==1, effect * rel_time, 0) + rnormal()

}
xtline Y, overlay legend(off)

/* TWFE */
if 1==1{
xtreg Y D t, fe             /* 73.11 (3.9  */
xtreg Y D i.t, fe           /*  77.59 (3.7) */
reghdfe Y D, absorb(id t)   /*  77.59 (3.7) */  
reg Y D i.t i.id            /*  77.59 (3.7) */
}

/* GB: bacondecomp */
if 1==1{
bacondecomp Y D, ddetail

Diff-in-diff estimate: 77.591   

DD Comparison              Weight      Avg DD Est
-------------------------------------------------
Earlier T vs. Later C       0.333          88.197
Later T vs. Earlier C       0.150         -93.692
T vs. Never treated         0.517         120.411
-------------------------------------------------
}

/* CD: did_multiplegt */
if 1==1{
did_multiplegt Y id t D, robust_dynamic cluster(id) breps(20)

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0608394   .3547955  -.7562387   .6345598         78         23 

did_multiplegt Y id t D, robust_dynamic dynamic(10) placebo(10) breps(20) cluster(id)

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0608394    .353078  -.7528723   .6311935         78         23 
    Effect_1 |   8.49767   .4331132   7.648768   9.346572         78         23 
    Effect_2 |  17.64773   .4398626    16.7856   18.50986         78         23 
    Effect_3 |   25.9377   .7664737   24.43541   27.43998         78         23 
    Effect_4 |  34.62362   1.190048   32.29113   36.95611         75         23 
    Effect_5 |  42.85682    1.51316   39.89103   45.82262         64         19 
    Effect_6 |  51.93103   1.832343   48.33964   55.52242         64         19 
    Effect_7 |  60.13327   2.396984   55.43518   64.83136         64         19 
    Effect_8 |  68.82446   2.625587   63.67831   73.97061         64         19 
    Effect_9 |  77.30792   2.857373   71.70747   82.90837         64         19 
   Effect_10 |  85.78878   3.330466   79.26107   92.31649         55         19 
   Placebo_1 | -.1308918   .4742009  -1.060326    .798542         78         23 
   Placebo_2 |  .1944381   .3786051  -.5476278    .936504         78         23 
   Placebo_3 |  .0639963   .4074087  -.7345248   .8625173         78         23 
   Placebo_4 |  .2572878   .4032822  -.5331453   1.047721         78         23 
   Placebo_5 |  .0679468   .3813181  -.6794366   .8153302         78         23 
   Placebo_6 |  -.082143   .3154546   -.700434   .5361479         78         23 
   Placebo_7 |  -.271289   .3794009  -1.014915   .4723367         78         23 
   Placebo_8 |  .0338621   .3864144  -.7235101   .7912343         78         23 
   Placebo_9 | -.1010115   .3029614  -.6948159   .4927929         78         23 
  Placebo_10 |  .3842823   .3259317  -.2545438   1.023108         78         23 

event_plot e(estimates)#e(variances), default_look ///
    graph_opt(xtitle("Periods since the event") ytitle("Average causal effect") ///
    title("did_multiplegt") xlabel(-10(1)10)) stub_lag(Effect_#) stub_lead(Placebo_#) together
}   

/* CS: csdid: gvar=first_treat value for treated, =0 for not treated*/
if 1==1{
gen gvar = first_treat
recode gvar (. = 0)
csdid Y, ivar(id) time(t) gvar(gvar) notyet
/*Giant output of ATTs for each treated group*/
estat event, window(-10 10) estore(cs) /* Event study w/ 10-leads/10-lags*/
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0416478   .0426553     0.98   0.329     -.041955    .1252506
    Post_avg |   43.04438   1.193983    36.05   0.000     40.70422    45.38454
        Tm10 |   .3842823   .3615112     1.06   0.288    -.3242667    1.092831
         Tm9 |  -.1010115    .308461    -0.33   0.743    -.7055839    .5035609
         Tm8 |   .0338621   .3130682     0.11   0.914    -.5797404    .6474646
         Tm7 |   -.271289   .3434149    -0.79   0.430    -.9443698    .4017918
         Tm6 |   -.082143   .3231954    -0.25   0.799    -.7155943    .5513083
         Tm5 |   .0679468   .3355773     0.20   0.840    -.5897727    .7256663
         Tm4 |   .2572878   .3222909     0.80   0.425    -.3743907    .8889663
         Tm3 |   .0639963   .4214074     0.15   0.879    -.7619471    .8899396
         Tm2 |   .1944381   .3707239     0.52   0.600    -.5321673    .9210435
         Tm1 |  -.1308918   .4307277    -0.30   0.761    -.9751027     .713319
         Tp0 |  -.0608394   .3220462    -0.19   0.850    -.6920383    .5703595
         Tp1 |    8.49767   .3964781    21.43   0.000     7.720587    9.274753
         Tp2 |   17.64773   .4650298    37.95   0.000     16.73629    18.55917
         Tp3 |    25.9377   .5978201    43.39   0.000     24.76599     27.1094
         Tp4 |   34.62362   .9250424    37.43   0.000     32.81057    36.43667
         Tp5 |   42.85682   1.223002    35.04   0.000     40.45978    45.25386
         Tp6 |   51.93103   1.529193    33.96   0.000     48.93387    54.92819
         Tp7 |   60.13327   1.804358    33.33   0.000     56.59679    63.66975
         Tp8 |   68.82446   1.982765    34.71   0.000     64.93831    72.71061
         Tp9 |   77.30792   2.264938    34.13   0.000     72.86872    81.74712
        Tp10 |   85.78878    2.61102    32.86   0.000     80.67128    90.90629
------------------------------------------------------------------------------

event_plot cs, default_look graph_opt(xtitle("Periods since the event") ytitle("Average effect") ///
    title("csdid") xlabel(-10(1)10)) stub_lag(Tp#) stub_lead(Tm#) together   
}

/* BJS: did_imputation */
if 1==1{
did_imputation Y i t first_treat, horizons(0/10) pretrend(10)
did_imputation Y i t first_treat, horizons(0/10) pretrend(10) minn(0) /* correct*/
                                                Number of obs     =      1,438
------------------------------------------------------------------------------
           Y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        tau0 |   .0787163   .2677837     0.29   0.769      -.44613    .6035626
        tau1 |   8.637227   .2815466    30.68   0.000     8.085406    9.189048
        tau2 |   17.78728   .2329168    76.37   0.000     17.33078    18.24379
        tau3 |   26.07725   .2293303   113.71   0.000     25.62777    26.52673
        tau4 |   34.76562   .2815757   123.47   0.000     34.21374     35.3175
        tau5 |   42.93274   .2848896   150.70   0.000     42.37437    43.49111
        tau6 |   52.00695   .2787334   186.58   0.000     51.46064    52.55326
        tau7 |   60.20919   .2526519   238.31   0.000       59.714    60.70438
        tau8 |   68.90038   .2317813   297.26   0.000      68.4461    69.35466
        tau9 |   77.38383   .2652313   291.76   0.000     76.86399    77.90368
       tau10 |   85.83142    .309541   277.29   0.000     85.22473    86.43811
        pre1 |   .1206052   .2867599     0.42   0.674    -.4414338    .6826443
        pre2 |   .2116369   .3322261     0.64   0.524    -.4395143    .8627881
        pre3 |   .0601094     .28101     0.21   0.831      -.49066    .6108789
        pre4 |   .0568874   .2810549     0.20   0.840    -.4939701    .6077449
        pre5 |  -.2050823   .2643479    -0.78   0.438    -.7231945      .31303
        pre6 |  -.3225205   .2344532    -1.38   0.169    -.7820403    .1369993
        pre7 |  -.2239894   .2512797    -0.89   0.373    -.7164885    .2685097
        pre8 |    .052194   .2244509     0.23   0.816    -.3877217    .4921096
        pre9 |   .0285485   .2359939     0.12   0.904     -.433991    .4910879
       pre10 |   .0962904   .2315309     0.42   0.677    -.3575018    .5500827
------------------------------------------------------------------------------

event_plot cs, default_look graph_opt(xtitle("Periods since the event") ytitle("Average effect") ///
    title("csdid") xlabel(-10(1)10)) stub_lag(Tp#) stub_lead(Tm#) together   
}

/* eventstudyinteract (Sun and Abraham 2020) */
/*PROBLEM eventstudyinteract: 10 leads/lags & drop 1st lead:*/
if 1==1{
    // leads
    cap drop F_*
    forval x = 2/10 {  // drop the first lead
        gen F_`x' = rel_time == -`x'
    }

    //lags
    cap drop L_*
    forval x = 0/10 {
        gen L_`x' = rel_time ==  `x'
    }

gen never_treat = first_treat==.
sum first_treat
gen last_cohort = first_treat==r(max) // dummy for the latest- or never-treated cohort  
eventstudyinteract Y L_* F_*, vce(cluster id) absorb(id t) cohort(first_treat) control_cohort(never_treat)  

           Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         L_0 |  -82.83641   9.395219    -8.82   0.000    -102.0518   -63.62103
         L_1 |  -72.09706   9.606305    -7.51   0.000    -91.74416   -52.44996
         L_2 |  -66.28563   9.387735    -7.06   0.000     -85.4857   -47.08555
         L_3 |  -59.53088   9.654911    -6.17   0.000    -79.27739   -39.78437
         L_4 |  -57.36642   9.575378    -5.99   0.000    -76.95027   -37.78257
         L_5 |  -39.75675   9.060562    -4.39   0.000    -58.28768   -21.22581
         L_6 |  -33.14397   8.537269    -3.88   0.001    -50.60465    -15.6833
         L_7 |  -27.89437    7.86436    -3.55   0.001     -43.9788   -11.80995
         L_8 |  -23.26935   7.097292    -3.28   0.003    -37.78494   -8.753756
         L_9 |  -9.696573   5.205239    -1.86   0.073    -20.34248    .9493362
        L_10 |  -11.75181   7.008037    -1.68   0.104    -26.08486    2.581231
         F_2 |  -74.56978   7.895172    -9.44   0.000    -90.71722   -58.42234
         F_3 |  -73.05913   7.860883    -9.29   0.000    -89.13645   -56.98182
         F_4 |  -73.13007   7.492343    -9.76   0.000    -88.45363    -57.8065
         F_5 |  -69.47478   8.197212    -8.48   0.000    -86.23996    -52.7096
         F_6 |  -71.25639   7.298475    -9.76   0.000    -86.18344   -56.32933
         F_7 |  -67.85099   7.697344    -8.81   0.000    -83.59383   -52.10815
         F_8 |  -66.67338   7.647583    -8.72   0.000    -82.31445   -51.03232
         F_9 |    -65.677   7.560869    -8.69   0.000    -81.14071   -50.21328
        F_10 |   -64.7154   7.532503    -8.59   0.000     -80.1211    -49.3097

    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_#) together

sum first_treat if last_cohort==1
eventstudyinteract Y L_* F_* if t<`r(max)' & first_treat!=., vce(cluster id) absorb(id t) cohort(first_treat) control_cohort(last_cohort)       

    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_#) together

}

/* stackedev (Cengiz, Dube, Lindner, Zipperer 2019) */
/* PROBLEM stackedev: generate 10 leads and lags & no_treat var*/
if 1==1{
gen no_treat = first_treat==.           

    // leads
    cap drop F_*
    forval x = 1/10 {  // drop the first lead
        gen     F_`x' = rel_time == -`x'
        replace F_`x' = 0 if no_treat==1
    }

    //lags
    cap drop L_*
    forval x = 0/10 {
        gen     L_`x' = rel_time ==  `x'
        replace L_`x' = 0 if no_treat==1
    }

    ren F_1 ref  // reference year
stackedev Y F_* L_* ref, cohort(first_treat) time(t) never_treat(no_treat) unit_fe(id) clust_unit(id)

    event_plot, default_look graph_opt(xtitle("Periods since the event") ytitle("Average effect") xlabel(-10(1)10) ///
        title("stackedev")) stub_lag(L_#) stub_lead(F_#) together

}       

/* did2s (Gardner 2021) */  
/*did2s:              10 leads/lags & drop 1st lead:*/
if 1==1{
    // leads
    cap drop F_*
    forval x = 2/10 {  // drop the first lead
        gen F_`x' = rel_time == -`x'
    }

    //lags
    cap drop L_*
    forval x = 0/10 {
        gen L_`x' = rel_time ==  `x'
    }

did2s Y, first_stage(id t) second_stage(F_* L_*) treatment(D) cluster(id)

    event_plot, default_look graph_opt(xtitle("Periods since the event") ytitle("Average effect") xlabel(-10(1)10) ///
        title("did2s")) stub_lag(L_#) stub_lead(F_#) together
}

[asjadnaqvi]

Thanks @azev77! I randomly checked this space and found your post. I still plan to add more stuff to each package and combine them together in a separate post so this will be very useful.

[asjadnaqvi]

Added!