Compute pooled event-time treatment-effect estimates

For a fitted object from fetwfe(), etwfe(), or betwfe(), computes the pooled event-time treatment-effect estimates tau_E(e), defined as cohort-weighted averages of the cell-level treatment-effect estimates at each post-treatment event time e = t - g (where t is calendar time and g is the cohort's first-treated calendar time). Weights are sample-cohort-size weights (matching did::aggte(type = "dynamic") convention).

Standard errors combine two terms, mirroring the package's existing overall-ATT SE machinery: var_1(e) from regression-coefficient noise (computed via the same gram_inv machinery the package uses for cohort SEs, or the cluster-robust sandwich under se_type = "cluster"), and var_2(e) from cohort-probability noise (analog of the existing getSecondVarTermOLS / getSecondVarTermDataApp machinery, with the multinomial Jacobian restricted to cohorts valid at event time e). Combined as sqrt(var_1 + var_2) by default (asymptotically exact under paper Theorem te.asym.norm.thm(\(c'\)) / Assumption (\(\Psi\)-IF), which the package's default cohort-sample-proportions estimator satisfies); the conservative Cauchy-Schwarz bound sqrt(var_1 + var_2 + 2 sqrt(var_1 * var_2)) is available via se_type = "conservative" (for users with non-(\(\Psi\)-IF) propensity-score estimators). When indep_counts was supplied at fit time, the tight formula applies regardless of se_type (two-sample regime, Theorem (b)).

Usage

eventStudy(x, alpha = NULL, ci_type = NULL)

Arguments

x

A fitted object of class "fetwfe", "etwfe", or "betwfe".

alpha

(Optional) Significance level for confidence intervals. Defaults to x$alpha (the alpha used at fit time).

ci_type

(Optional) Character; one of "simultaneous" or "pointwise", or NULL (default). Controls whether the returned ci_low / ci_high are the event-study-family simultaneous (family-wise, uniform) band or the per-event-time pointwise Wald intervals. NULL inherits the fit's stored ci_type (so a fit made with the default ci_type = "simultaneous" yields simultaneous event-study bands, which print / summary / plot then display); for objects fitted before version 1.16.0 (no ci_type slot) NULL falls back to "pointwise". The se column is identical under both settings; the interval bounds, their critical-value multiplier, and the p_value differ (under "simultaneous" the p_value is the single-step max-T multiplicity- adjusted dual of the band, under "pointwise" the per-effect Wald p-value; #200). Degenerate event times (no valid contributing cohorts) carry NA bounds and NA p_value under both settings.

High-dimensional (p >= NT): the "simultaneous" band here is the analytic post-selection band on the selected support (or NA bounds on a gls = FALSE fit), not the uniformly-valid desparsified band. For the valid high-dimensional event-study band call simultaneousCIs() with family = "event_study", method = "bootstrap" (Theorem debiased.highdim.joint.thm, validated near-nominally in Faletto 2025).

Value

A data frame with class c("eventStudy", "data.frame") and columns:

event_time

Integer; event time e = t - g, ranging from 0 to T - 2.

n_cohorts

Integer; number of cohorts contributing to the pooled estimate at event time e.

estimate

Numeric; the pooled event-time ATT estimate.

se

Numeric; combined standard error.

ci_low

Numeric; lower bound of the (1 - alpha) Wald CI.

ci_high

Numeric; upper bound of the (1 - alpha) Wald CI.

p_value

Numeric; follows the fit's ci_type. Under "pointwise", the two-sided Wald p-value (2 * pnorm(-|estimate / se|)); under "simultaneous" (the default), the single-step max-T multiplicity-adjusted (family-wise) p-value matching the simultaneous band (#200). NA when se is 0 or NA.

Only post-treatment event times (e >= 0) are included; pre-treatment placebo periods would require an extended regression specification and are out of scope for this initial release.

See also

cohortStudy() for the parallel per-cohort accessor; cohortTimeATTs() for the fully disaggregated per-(cohort, time) accessor.

Examples

if (FALSE) { # \dontrun{
  coefs <- genCoefs(G = 3, T = 6, d = 2, density = 0.5, eff_size = 2)
  dat <- simulateData(coefs, N = 120, sig_eps_sq = 1, sig_eps_c_sq = 0.5, seed = 123)
  res <- fetwfeWithSimulatedData(dat)
  eventStudy(res)
} # }