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- W2341337475 abstract "This paper develops a method for inference in dynamic discrete choice models with serially correlated unobserved state variables. Estimation of these models involves com puting high-dimensional integrals that are present in the solution to the dynamic pro gram 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 es timation procedure." @default.
- W2341337475 created "2016-06-24" @default.
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- W2341337475 date "2009-01-01" @default.
- W2341337475 modified "2023-09-24" @default.
- W2341337475 title "NOTES AND COMMENTS INFERENCE IN DYNAMIC DISCRETE CHOICE MODELS WITH SERIALLY CORRELATED UNOBSERVED STATE VARIABLES" @default.
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