Nonparametric Bayes subject to overidentified moment conditions

A. Ronald Gallant

Journal of Econometrics, 2022, vol. 228, issue 1, 27-38

Abstract: 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.

Keywords: Method of moments; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 C14 C15 C32 C36 C58 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:228:y:2022:i:1:p:27-38

DOI: 10.1016/j.jeconom.2021.02.005

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