I am grateful to John Geweke, my dissertation advisor, for guidance and encouragement through the work on this project. I thank Charles Whiteman, Luke Tierney, and Jeffrey Rosenthal for helpful suggestions. I also thank Latchezar Popov for reading and discussing some of the proofs in this paper. Comments by Elena Pastorino, Yaroslav Litus, Marianna Kudlyak, a co-editor, and anonymous referees helped to improve the manuscript. I acknowledge financial support from the Economics Department graduate fellowship and the Seashore Dissertation fellowship at the University of Iowa. All remaining errors are mine.
Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables
Version of Record online: 6 OCT 2009
© 2009 The Econometric Society
Volume 77, Issue 5, pages 1665–1682, September 2009
How to Cite
Norets, A. (2009), Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables. Econometrica, 77: 1665–1682. doi: 10.3982/ECTA7292
- Issue online: 6 OCT 2009
- Version of Record online: 6 OCT 2009
- Manuscript received July, 2007; final revision received March, 2009.
- Dynamic discrete choice models;
- Bayesian estimation;
- nearest neighbors;
- random grids
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.