Econometrica: Sep 2009, Volume 77, Issue 5

Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables
p. 1665-1682

Andriy Norets

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‚Äźdimensional 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.

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Supplement to "Inference in Dynamic Discrete Choice Models with Serially Correlated Unobserved State Variables"

This supplement provides proofs for the manuscript.

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