We are very grateful to the editor and the anonymous referees for insightful comments. Thanks also go to Chris Ferrall, Chris Flinn, Wesley Hartmann, Mike Keane, Justin McCrary, Andriy Norets, Matthew Osborne, Peter Rossi, John Rust, and seminar participants at the UIUC, NYU, Ohio State University, University of Kansas, University of Michigan, University of Minnesota, SBIES, 2006 Quantitative Marketing and Economics Conference, and 2005 Econometrics Society World Congress for helpful comments on the earlier draft of the paper. We also thank SSHRC and FQRSC for financial support. All remaining errors are our own.
Bayesian Estimation of Dynamic Discrete Choice Models
Version of Record online: 2 DEC 2009
© 2009 The Econometric Society
Volume 77, Issue 6, pages 1865–1899, November 2009
How to Cite
Imai, S., Jain, N. and Ching, A. (2009), Bayesian Estimation of Dynamic Discrete Choice Models. Econometrica, 77: 1865–1899. doi: 10.3982/ECTA5658
- Issue online: 2 DEC 2009
- Version of Record online: 2 DEC 2009
- Manuscript received January, 2005; final revision received May, 2009.
- Bayesian estimation;
- dynamic programming;
- discrete choice models;
- Markov chain Monte Carlo
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 the number of grid points on the state variable is small per solution-estimation iteration, 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.