Bayesian and Frequentist Inference in Partially Identified Models

Hyungsik Moon () and Frank Schorfheide

No 14882, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: A large sample approximation of the posterior distribution of partially identified structural parameters is derived for models that can be indexed by a finite-dimensional reduced form parameter vector. It is used to analyze the differences between frequentist confidence sets and Bayesian credible sets in partially identified models. A key difference is that frequentist set estimates extend beyond the boundaries of the identified set (conditional on the estimated reduced form parameter), whereas Bayesian credible sets can asymptotically be located in the interior of the identified set. Our asymptotic approximations are illustrated in the context of simple moment inequality models and a numerical illustration for a two-player entry game is provided.

JEL-codes: C11 C32 C35 (search for similar items in EconPapers)
Date: 2009-04
New Economics Papers: this item is included in nep-cba and nep-ecm
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Published as Hyungsik Roger Moon & Frank Schorfheide, 2012. "Bayesian and Frequentist Inference in Partially Identified Models," Econometrica, Econometric Society, vol. 80(2), pages 755-782, 03.

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Journal Article: Bayesian and Frequentist Inference in Partially Identified Models (2012)
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