Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables
Econometrica · 2009 · Volume 77 · Issue 5
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.
Keywords
Dynamic discrete choice models, Bayesian estimation, MCMC, nearest neighbors, random grids
Enable JavaScript for citation tools and additional paper interactions.