Event study in Stata

[Swarup8289]

First, thanks a lot for putting this out for learners like us. I was wondering if i can use the following for repeated cross sectional data as well?

Code copy and pasted from the diff-in-diff event study section************

use "https://raw.githubusercontent.com/LOST-STATS/LOST-STATS.github.io/master/Model_Estimation/Data/Event_Study_DiD/bacon_example.dta", clear

• create the lag/lead for treated states

• fill in control obs with 0

• This allows for the interaction between treat and time_to_treat to occur for each state.

• Otherwise, there may be some NAs and the estimations will be off.
  g time_to_treat = year - _nfd
  replace time_to_treat = 0 if missing(_nfd)

• this will determine the difference

• btw controls and treated states
  g treat = !missing(_nfd)

• Stata won't allow factors with negative values, so let's shift

• time-to-treat to start at 0, keeping track of where the true -1 is
  summ time_to_treat
  g shifted_ttt = time_to_treat - r(min)
  summ shifted_ttt if time_to_treat == -1
  local true_neg1 = r(mean)

• Regress on our interaction terms with FEs for group and year,

• clustering at the group (state) level

• use ib# to specify our reference group
  reghdfe asmrs ib`true_neg1'.shifted_ttt pcinc asmrh cases, a(stfips year) vce(cluster stfips)

• Pull out the coefficients and SEs
  g coef = .
  g se = .
  levelsof shifted_ttt, l(times)
  foreach t in times' { replace coef = _b[t'.shifted_ttt] if shifted_ttt == t' replace se = _se[t'.shifted_ttt] if shifted_ttt == `t'
  }

• Make confidence intervals
  g ci_top = coef+1.96se
  g ci_bottom = coef - 1.96se

• Limit ourselves to one observation per quarter

• now switch back to time_to_treat to get original timing
  keep time_to_treat coef se ci_*
  duplicates drop

sort time_to_treat

• Create connected scatterplot of coefficients
• with CIs included with rcap
• and a line at 0 both horizontally and vertically
  summ ci_top
  local top_range = r(max)
  summ ci_bottom
  local bottom_range = r(min)

twoway (sc coef time_to_treat, connect(line)) ///
(rcap ci_top ci_bottom time_to_treat) ///
(function y = 0, range(time_to_treat)) ///
(function y = 0, range(bottom_range' top_range') horiz), ///
xtitle("Time to Treatment") caption("95% Confidence Intervals Shown")

[NickCH-K]

Yep, should work fine for DID performed with repeated cross section data.