Bayesian Estimation of Cohort-Time-Stratum Specific Effects in Staggered Difference-in-Differences

Siddhartha Chib and Kenichi Shimizu

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Abstract: Difference-in-Differences designs with staggered treatment adoption are widely used to study heterogeneous treatment effects across cohorts and time periods. We develop a probabilistic framework for estimating potentially high-dimensional ATT arrays that vary across cohorts, periods, and strata defined by baseline covariates. The framework jointly estimates subgroup-specific treatment effects through a unified likelihood-based model, stabilizing inference in sparse cohort-by-time-by-stratum settings. We establish a Bernstein-von Mises theorem for the ATT array, implying asymptotically valid frequentist coverage of posterior credible intervals. Simulations and an application to minimum wage increases and teen employment demonstrate meaningful finite-sample improvements and important subgroup heterogeneity.

Date: 2025-05, Revised 2026-05
New Economics Papers: this item is included in nep-ecm and nep-inv
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