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Parametric Bootstrap Inference for GSC (Xu 2017 S.3)

Source: R/gsc.R
gsc_boot.Rd

Generates the null distribution of the ATT under H0 (no treatment effect) by parametric resampling from the estimated IFE factor model. Under H0, both the control panel and treated unit are generated from the fitted factor model with homoskedastic noise. When the fit includes covariate adjustment (beta), the covariate contribution is included in the simulated DGP and re-estimated in each bootstrap replicate.

Usage

gsc_boot(fit, B = 499L, alpha = 0.05, seed = NULL)

Arguments

fit

A coresynth object from scm_fit() with method = "gsc".

B

Bootstrap replications (default 499L).

alpha

Significance level for the confidence interval (default 0.05).

seed

RNG seed for reproducibility (default NULL).

Value

A list with:

  • p_value: Two-sided p-value: mean(|ATT*| >= |ATT_obs|)

  • ci_lower: Lower bound of (1-alpha)*100% bootstrap CI

  • ci_upper: Upper bound of (1-alpha)*100% bootstrap CI

  • se: Bootstrap standard error

  • boot_dist: Numeric vector of length B (bootstrap ATT* values)

  • att_obs: Observed ATT from the original fit