Combining jwdid event aggreagations with event_plot

[Tom Ford]

Hello,
I am trying to estimate a DiD event plot model and I would like to put into the same plot both my TWFE estiamtes as well as the results of the approach introduced by Wooldridge 2021 (ETWFE).

Does someon know a workaround to do this?

Here is an example
First I estimate my ETWFE model

* Upload baseline data
ssc install frause
frause mpdta.dta, clear

* jwdid estimates
jwdid lemp, ivar(countyreal) tvar(year) gvar(first_treat) never
estat event,  estore(etwfe)

then I create variables necessary to estimate a simple TWFE

* create lags and lead dummies to estimate the simple twfe model
decode __event__, gen(ttt)
destring ttt,replace

summ ttt
local relmin = abs(r(min))
local relmax = abs(r(max))

    // leads
    cap drop F_*
    forval x = 2/`relmin' {  // drop the first lead
        gen F_`x' = ttt == -`x'
    }

    
    //lags
    cap drop L_*
    forval x = 0/`relmax' {
        gen L_`x' = ttt ==  `x'
    }

And estimate the simple TWFE model

reghdfe lemp L_* F_*, absorb(countyreal year)
eststo twfe

Now how can I combine the results of my estimation into a single plot using event_plot?

here is one attempt

 est restore etwfe
(results etwfe are active now)

. matrix etwfe_b = e(b_iw)

. matrix etwfe_v = e(V_iw)

.
.
. event_plot twfe etwfe_b#etwfe_v, ///
>     stub_lag(L_# L__event__#) ///
>     stub_lead(F_# F__event__#) //
No estimates found for the model "etwfe_b#etwfe_v". Make sure you have specified stub_lag and stub_lead correctly.
r(498);

I think the problem relates to the fact that the way jwdid estimates are saved does not differentiates pre-post treatment and the solution would be to do a workaround to rename the estimates. Would anyone be able to help me?

. matrix list jw_b

jw_b[1,29]
             1.          2.          3.          4.          5.          6.          7.          8.          9.
     __event__   __event__   __event__   __event__   __event__   __event__   __event__   __event__   __event__
y1    .0020739  -.00535054   .00346115   .01881496   .01907641  -.00106849  -.02179202  -.01319871           0

            10.         11.         12.         13.         14.         15.         16.         17.         18.
     __event__   __event__   __event__   __event__   __event__   __event__   __event__   __event__   __event__
y1  -.00500839   -.0008726  -.00085282   -.0135531  -.00487692   .01775061  -.00271763   .00220589  -.01344839

            19.         20.         21.         22.         23.         24.         25.         26.         27.
     __event__   __event__   __event__   __event__   __event__   __event__   __event__   __event__   __event__
y1   .01331134   .02196031   .01573812   .00941126   .02729782   .01452821   .02321013  -.02210793   .01325247

            28.         29.
     __event__   __event__
y1  -.01168418  -.09846029

thank you very much in advance for your help