Nonparametric Bayes subject to overidentified moment conditions

Nonparametric Bayesian estimation subject to overidentified moment equations is a challenge because the support of the posterior is a manifold of lower dimension than the number of model parameters. The manifold therefore has Lebesgue measure zero thus inhibiting the use of the most commonly used Ba...

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Bibliographic Details
Published inJournal of econometrics Vol. 228; no. 1; pp. 27 - 38
Main Author Gallant, A. Ronald
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.05.2022
Elsevier Sequoia S.A
Subjects
Algorithms
Bayesian analysis
Bayesian inference
Bayesian theory
econometrics
Estimating techniques
Markov analysis
Markov chain
Method of moments
Monte Carlo simulation
Nonparametric statistics
C11
C32
C15
C14
C36
C58
Bayesian inference
Method of moments
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Summary:Nonparametric Bayesian estimation subject to overidentified moment equations is a challenge because the support of the posterior is a manifold of lower dimension than the number of model parameters. The manifold therefore has Lebesgue measure zero thus inhibiting the use of the most commonly used Bayesian estimation method: MCMC (Markov Chain Monte Carlo). This study proposes an effective MCMC algorithm and algorithms for estimating scale and the normalizing constant. The algorithms are illustrated with two illustrative applications.
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2021.02.005