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Bayesian Estimation Of Dynamic Discrete Choice Models

Andrew Ching, Susumu Imai and Neelam Jain ()

No 1118, Working Paper from Economics Department, Queen's University

Abstract: We propose a new methodology for structural estimation of dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though per solution-estimation iteration, the number of grid points on the state variable is small, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "Curse of Dimensionality". We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.

Keywords: Bayesian Estimation; Dynamic Discrete Choice Model; Dynamic Programming; Markov Chain Monte Carlo; Bayesian Dynamic Programming Estimation (search for similar items in EconPapers)
JEL-codes: C51 C61 C63 L00 (search for similar items in EconPapers)
Pages: 77 pages
Date: 2006-12
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1118.pdf First version 2006 (application/pdf)

Related works:
Journal Article: Bayesian Estimation of Dynamic Discrete Choice Models (2009) Downloads
Working Paper: Bayesian Estimation of Dynamic Discrete Choice Models (2005) Downloads
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