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Honest (relative-magnitudes) sensitivity for event-study estimates

Source: R/did.R
morie_did_honest_sensitivity.Rd

Conservative Rambachan-Roth (2023) relative-magnitudes bounds on an event-study coefficient: the post-treatment bias from a parallel-trends violation is bounded by \(\bar M\) times the largest observed pre-treatment deviation, and the confidence interval is widened by that bound. \(\bar M = 0\) reproduces the conventional CI; \(\bar M = 1\) allows post-treatment violations as large as the worst pre-trend. This is the conservative (fixed-bias) version of the relative-magnitudes parameterization, anchored on the estimated pre-period coefficients.

Usage

morie_did_honest_sensitivity(
  event_study,
  m_bar_range = seq(0, 2, 0.5),
  target_time = 0L,
  alpha = 0.05
)

Arguments

event_study

The result of morie_did_event_study (or any list with a coefficients data frame containing relative_time, estimate, std_error).

m_bar_range

Numeric vector of \(\bar M\) values (default seq(0, 2, 0.5)).

target_time

Relative time of the post-treatment coefficient to bound (default 0, the onset period).

alpha

Significance level.

Value

A data frame with columns m_bar, estimate, ci_lower, ci_upper, covers_zero, plus a breakdown_m_bar attribute (the smallest evaluated \(\bar M\) whose interval covers zero).

References

Rambachan, A., & Roth, J. (2023). A more credible approach to parallel trends. Review of Economic Studies, 90(5), 2555–2591.

See also

morie_did_sensitivity_analysis for the 2x2 \(\delta \hat\sigma\) parameterization.

Examples

set.seed(20)
df <- expand.grid(unit = 1:60, time = 1:8)
df$treat_time <- ifelse(df$unit <= 30, 5, Inf)
df$d <- as.integer(df$time >= df$treat_time)
df$y <- 0.5 * df$time + 1.5 * df$d + rnorm(nrow(df), sd = 0.5)
es <- morie_did_event_study(df, "y", "unit", "time", "treat_time",
                            leads = 3L, lags = 3L)
out <- morie_did_honest_sensitivity(es, m_bar_range = c(0, 1, 5))
out
#>   m_bar estimate  ci_lower ci_upper covers_zero
#> 1     0 1.408047 0.9971091 1.818985       FALSE
#> 2     1 1.408047 0.8629814 1.953113       FALSE
#> 3     5 1.408047 0.3264705 2.489624       FALSE