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Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables

Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables This paper develops a method for inference in dynamic discrete choice models with serially correlated unobserved state variables. Estimation of these models involves computing high‐dimensional integrals that are present in the solution to the dynamic program and in the likelihood function. First, the paper proposes a Bayesian Markov chain Monte Carlo estimation procedure that can handle the problem of multidimensional integration in the likelihood function. Second, the paper presents an efficient algorithm for solving the dynamic program suitable for use in conjunction with the proposed estimation procedure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Econometrica Wiley

Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables

Econometrica , Volume 77 (5) – Sep 1, 2009

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References (33)

Publisher
Wiley
Copyright
Copyright © 2009 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0012-9682
eISSN
1468-0262
DOI
10.3982/ECTA7292
Publisher site
See Article on Publisher Site

Abstract

This paper develops a method for inference in dynamic discrete choice models with serially correlated unobserved state variables. Estimation of these models involves computing high‐dimensional integrals that are present in the solution to the dynamic program and in the likelihood function. First, the paper proposes a Bayesian Markov chain Monte Carlo estimation procedure that can handle the problem of multidimensional integration in the likelihood function. Second, the paper presents an efficient algorithm for solving the dynamic program suitable for use in conjunction with the proposed estimation procedure.

Journal

EconometricaWiley

Published: Sep 1, 2009

Keywords: ; ; ; ;

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