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Bayesian and Frequentist Inference in Partially Identified Models

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  • Hyungsik Roger Moon
  • Frank Schorfheide

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.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 80 (2012)
Issue (Month): 2 (03)
Pages: 755-782

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Handle: RePEc:ecm:emetrp:v:80:y:2012:i:2:p:755-782

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  14. Canay, Ivan A., 2010. "EL inference for partially identified models: Large deviations optimality and bootstrap validity," Journal of Econometrics, Elsevier, vol. 156(2), pages 408-425, June.
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Citations

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Cited by:
  1. Epstein, Larry G. & Seo, Kyoungwon, 2014. "De Finetti meets Ellsberg," Research in Economics, Elsevier, vol. 68(1), pages 11-26.
  2. Kyungchul Song, 2009. "Point Decisions for Interval-Identified Parameters," PIER Working Paper Archive 09-036, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  3. Atsushi Inoue & Mototsugu Shintania, 2014. "Quasi-Bayesian Model Selection," Departmental Working Papers 1402, Southern Methodist University, Department of Economics.
  4. Liao, Yuan & Simoni, Anna, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," MPRA Paper 43262, University Library of Munich, Germany.

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