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Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables
Author(s) -
Norets Andriy
Publication year - 2009
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta7292
Subject(s) - inference , markov chain monte carlo , likelihood function , computer science , bayesian inference , markov chain , discrete choice , bayesian probability , mathematical optimization , function (biology) , econometrics , algorithm , estimation theory , mathematics , machine learning , artificial intelligence , evolutionary biology , biology
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.