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grantmcdermott/baker.R

Created February 21, 2022 01:29
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Generate the "Baker" simulated dataset for staggered treatment effects
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baker.R
set.seed(1234)
# Create the base dataset as a cross-join of 1,000 firms over 30 periods
baker = expand.grid(n = 1:30, id = 1:1000)
# Add additional columns
baker =
baker |>
within({
year = n + 1980 - 1
state = 1 + (id-1) %/% 25
firms = runif(id*year, 0, 5)
group = 1 + (state-1) %/% 10
treat_date = 1980 + group*6
time_til = year - treat_date
treat = time_til>=0
e = rnorm(id*year, 0, 0.5^2)
te = rnorm(id*year, 10-2*(group-1), 0.2^2)
y = firms + n + treat*te*(year - treat_date + 1) + e
y2 = firms + n + te*treat + e
})
@kylebutts
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So, I don't have too much time to dig into this all right now, buttttt

  1. I think my Sun and Abraham code is wrong and it's on my todo list to fix for event_study, so I think that might be user-error
  2. BJS and did2s will produce identical estimates in the post-period. I'm not sure exactly the reason, but BJS estimates the pre-period in a separate regression:
pre_est <- fixest::feols(pre_formula, data[data$zz000treat == 0, ], weights = weights_vector[data$zz000treat == 0], warn=FALSE, notes=FALSE)

source: https://github.com/kylebutts/didimputation/blob/0330e81be3267d8c759297b0dd1ebc3480aac7db/R/did_imputation.R#L270

I think that is causing the weird level shift, but can't really place a finger on what the problem is there

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