Journal article
Bayesian inference for partially identified smooth convex models
Journal of econometrics, Vol.211(2), pp.338-360
08/01/2019
DOI: 10.1016/j.jeconom.2019.03.001
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.
Details
- Title: Subtitle
- Bayesian inference for partially identified smooth convex models
- Creators
- Yuan Liao - Rutgers, The State University of New JerseyAnna Simoni - Centre de Recherche en Économie et Statistique
- Resource Type
- Journal article
- Publication Details
- Journal of econometrics, Vol.211(2), pp.338-360
- DOI
- 10.1016/j.jeconom.2019.03.001
- ISSN
- 0304-4076
- eISSN
- 1872-6895
- Publisher
- Elsevier
- Number of pages
- 23
- Language
- English
- Date published
- 08/01/2019
- Academic Unit
- Economics
- Record Identifier
- 9984936820702771
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