Bayesian inference for partially identified smooth convex models

Yuan Liao and Anna Simoni

Journal of Econometrics, 2019, vol. 211, issue 2, 338-360

Abstract: This paper proposes novel Bayesian procedures for partially identified models when the identified set is convex with a smooth boundary, whose support function is locally smooth with respect to the data distribution. Using the posterior of the identified set, we construct Bayesian credible sets for the identified set, the partially identified parameter and their scalar transformations. These constructions, based on the support function, benefit from several computationally attractive algorithms when the identified set is convex, and are proved to have valid large sample frequentist coverages. These results are based on a local linear expansion of the support function of the identified set. We provide primitive conditions to verify such an expansion.

Keywords: Partial identification; Bayesian credible sets; Bernstein–von Mises theorem; Support function; Moment inequality models (search for similar items in EconPapers)
JEL-codes: C10 C11 C13 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Working Paper: Bayesian inference for partially identified smooth convex models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:211:y:2019:i:2:p:338-360

DOI: 10.1016/j.jeconom.2019.03.001

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