Inference for Synthetic Difference-in-Differences — sdid

Computes standard errors and p-values for a SDID estimate using one of three methods: permutation placebo test (Algorithm 4), cluster bootstrap (Algorithm 2), or leave-one-out jackknife (Algorithm 3), following Clarke et al. (2023).

Usage

sdid_inference(
  fit,
  method = c("placebo", "bootstrap", "jackknife", "jackknife_global"),
  n_boot = 200L,
  level = 0.95,
  alternative = c("two.sided", "greater", "less"),
  seed = NULL
)

Arguments

fit: A coresynth object with method = "sdid" (sharp adoption only).
method: Inference method: "placebo" (permutation), "bootstrap", or "jackknife".
n_boot: Number of bootstrap replications (only for method = "bootstrap").
level: Confidence level for the interval (only for method = "bootstrap" or "jackknife").
alternative: Direction of the alternative hypothesis: "two.sided", "greater", or "less".
seed: Integer seed for reproducibility (only for method = "bootstrap").

Value

A list with:

estimate: The SDID point estimate.
se: Standard error (bootstrap / jackknife only).
p_value: Permutation or normal-approximation p-value.
ci_lower, ci_upper: Confidence interval bounds (bootstrap / jackknife).
method: The inference method used.
n_controls: Number of control units.
alternative: The alternative hypothesis direction.
placebo_effects: Named vector of LOO placebo effects (placebo only).
boot_ests: Bootstrap estimate distribution (bootstrap only).