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Per-period (event-study) effects from a TROP fit

Source: R/trop.R
trop_event_study.Rd

Decomposes a fitted trop() ATT into per-period treatment effects indexed by event time (periods relative to each unit's first treated period) and attaches pointwise standard errors and confidence intervals. The same resampling used for the overall SE is reused: each resample's per-cell effects are aggregated by event time rather than into a single mean, so no extra modelling assumptions are introduced. With pre_periods = TRUE the pre-treatment gaps are returned as placebo / pre-trend points (a flat, near-zero pre-period profile supports the design).

Usage

trop_event_study(
  object,
  se = c("bootstrap", "jackknife", "none"),
  pre_periods = TRUE,
  control = NULL,
  ...
)

Arguments

object

A trop fit from trop().

se

Standard-error method for the per-period effects: "bootstrap" (default; unit-level block bootstrap, stratified unless trop_control(boot_resample = "controls")), "jackknife" (delete-one-unit over every unit, as for the overall ATT), or "none".

pre_periods

Logical; include pre-treatment event times as placebo / pre-trend points (default TRUE).

control

A list of solver/CV/bootstrap settings from trop_control(); defaults to trop_control(). Use it to set n_boot, boot_ci, seed, and workers for the resampling.

...

Unused.

Value

An object of class trop_event_study: a list whose estimates is a data.frame with one row per event time (event_time, estimate, std.error, conf.low, conf.high, n_cells, period), plus the overall att, the se.method, and metadata.

Details

Because the treated composition varies across bootstrap resamples and each event time draws on relatively few cells, the per-period intervals are wider than the overall ATT interval and cover each point individually (they are not simultaneous bands).

References

Athey, S., Imbens, G. W., Qu, Z., & Viviano, D. (2026). Triply Robust Panel Estimators. Journal of Applied Econometrics. doi:10.1002/jae.70061 .

See also

autoplot.trop_event_study() to plot the result.

Examples

# \donttest{
df  <- sim_panel(N = 16, T = 9, n_treated = 4, t0 = 6, att = 3, seed = 1)
# fixed penalties keep the example fast; omit `lambda` to cross-validate
fit <- trop(df, "y", "w", "id", "t", se = "none",
            lambda = list(time = 0.1, unit = 0.5, nn = 2))
es  <- trop_event_study(fit, se = "bootstrap", pre_periods = TRUE,
                        control = trop_control(n_boot = 20L, seed = 1))
es
#> TROP event study (per-period ATT)
#>   overall ATT = 3.2006
#>   event times: 4 post-treatment, 5 pre-treatment (placebo)
#>   SE: bootstrap (20 reps); 95% pointwise CI
#>  event_time estimate std.error conf.low conf.high n_cells period
#>          -5 -0.38700    0.2014 -0.63272   0.05746       4    pre
#>          -4  0.23676    0.1404 -0.06144   0.39595       4    pre
#>          -3  0.10638    0.1967 -0.20472   0.44004       4    pre
#>          -2  0.04075    0.1970 -0.27970   0.37886       4    pre
#>          -1 -0.03997    0.2463 -0.46544   0.33532       4    pre
#>           0  3.19224    0.3285  2.73900   3.85245       4   post
#>           1  3.24039    0.4329  2.50574   3.99350       4   post
#>           2  3.74927    0.5909  2.94867   4.79362       4   post
#>           3  2.94782    0.6199  2.00870   3.98453       4   post
# }