Bayesian Estimation of Dynamic Discrete Choice Models

127 Pages Posted: 9 Apr 2008 Last revised: 28 Dec 2009

See all articles by Susumu Imai

Susumu Imai

Queen's University - Department of Economics

Neelam Jain

City University London

Andrew T. Ching

Johns Hopkins University - Carey Business School

Abstract

We propose a new methodology for structural estimation of infinite horizon 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 Programming, Discrete Choice Models

JEL Classification: C11, C35, C61, D90

Suggested Citation

Imai, Susumu and Jain, Neelam and Ching, Andrew T., Bayesian Estimation of Dynamic Discrete Choice Models. Econometrica, Vol. 77, No. 6, pp. 1865-1899, November 2009, Available at SSRN: https://ssrn.com/abstract=1118130

Susumu Imai

Queen's University - Department of Economics ( email )

99 University Avenue
Kingston K7L 3N6, Ontario
Canada

HOME PAGE: http://qed.econ.queensu.ca/pub/faculty/imai/

Neelam Jain (Contact Author)

City University London ( email )

Northampton Square
London, EC1V OHB
United Kingdom

Andrew T. Ching

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

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