Journal article
BAYESIAN ANALYSIS IN MOMENT INEQUALITY MODELS
The Annals of statistics, Vol.38(1), pp.275-316
02/01/2010
DOI: 10.1214/09-AOS714
Abstract
This paper presents a study of the large-sample behavior of the posterior distribution of a structural parameter which is partially identified by moment inequalities. The posterior density is derived based on the limited information likelihood. The posterior distribution converges to zero exponentially fast on any delta-contraction Outside the identified region. Inside, if is bounded below by a positive constant if the identified region is assumed to have a nonempty interior. Our simulation evidence indicates that the Bayesian approach has advantages over frequentist methods, in the sense that, with a proper choice of the prior, the posterior provides more information about the true parameter inside the identified region. We also address the problem of moment and model selection. Our optimality criterion is the maximum posterior procedure and we show that, asymptotically, it selects the true moment/model combination with the most moment inequalities and the simplest model.
Details
- Title: Subtitle
- BAYESIAN ANALYSIS IN MOMENT INEQUALITY MODELS
- Creators
- Yuan Liao - Northwestern UniversityWenxin Jiang - Northwestern University
- Resource Type
- Journal article
- Publication Details
- The Annals of statistics, Vol.38(1), pp.275-316
- DOI
- 10.1214/09-AOS714
- ISSN
- 0090-5364
- eISSN
- 2168-8966
- Publisher
- Inst Mathematical Statistics
- Number of pages
- 42
- Language
- English
- Date published
- 02/01/2010
- Academic Unit
- Economics
- Record Identifier
- 9984936813302771
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