Logo
Munich Personal RePEc Archive

Posterior consistency of nonparametric conditional moment restricted models

Liao, Yuan and Jiang, Wenxin (2011): Posterior consistency of nonparametric conditional moment restricted models. Published in: Annals of Statistics , Vol. 39, No. 6 (2011): pp. 3003-3031.

[thumbnail of MPRA_paper_38700.pdf]
Preview
PDF
MPRA_paper_38700.pdf

Download (392kB) | Preview

Abstract

This paper addresses the estimation of the nonparametric conditional moment restricted model that involves an infinite-dimensional parameter g0. We estimate it in a quasi-Bayesian way, based on the limited information likelihood, and investigate the impact of three types of priors on the posterior consistency: (i) truncated prior (priors supported on a bounded set), (ii) thin-tail prior (a prior that has very thin tail outside a growing bounded set) and (iii) normal prior with nonshrinking variance. In addition, g0 is allowed to be only partially identified in the frequentist sense, and the parameter space does not need to be compact. The posterior is regularized using a slowly growing sieve dimension, and it is shown that the posterior converges to any small neighborhood of the identified region. We then apply our results to the nonparametric instrumental regression model. Finally, the posterior consistency using a random sieve dimension parameter is studied.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.