#2274. Robust Bayesian Inference for Set-Identified Models

August 2026publication date
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Journal’s subject area:
Economics and Econometrics;
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Abstract:
This paper reconciles the asymptotic disagreement between Bayesian and frequentist inference in set-identified models by adopting a multiple-prior (robust) Bayesian approach. We propose new tools for Bayesian inference in set-identified models and show that they have a well-defined posterior interpretation in finite samples and are asymptotically valid from the frequentist perspective. The main idea is to construct a prior class that removes the source of the disagreement.
Keywords:
asymptotic coverage; consistency; credible region; identified set; identifying restrictions; impulse-response analysis

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