Regression modelQuasi-experimental / causal inference

Bayesian Event Study Design

Bayesian Event Study Design extends the classical event study framework by replacing frequentist significance testing with a full Bayesian inferential framework. It estimates how an event (policy change, announcement, shock) alters an outcome trajectory by learning a prior model from the estimation window and updating it with observed data, yielding posterior distributions over abnormal effects and cumulative causal impacts with full uncertainty quantification.

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Sources

  1. Sorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, 45(2), 186-207. DOI: 10.1007/s11747-017-0516-y
  2. Glassman, M., & McAfee, R. B. (1996). Bayesian estimation of abnormal stock returns. Journal of Business & Economic Statistics, 10(3), 321-332. link

Related methods

ScholarGateBayesian Event Study Design (Bayesian Event Study Design for Causal Inference). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/bayesian-event-study-design