A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models

Andrew Ching (), Susumu Imai (), Masakazu Ishihara () and Neelam Jain ()
Additional contact information
Masakazu Ishihara: University of Toronto

No 1201, Working Papers from Queen's University, Department of Economics

Abstract: This paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed by Imai Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm, which solves the DDP model and estimates its structural parameters simultaneously. The main computational advantage of this estimation algorithm is the efficient use of information obtained from the past iterations. In the conventional Nested Fixed Point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming algorithm extensively uses the computational results obtained from the past iterations to help solving the DDP model at the current iterated parameter values. Consequently, it significantly alleviates the computational burden of estimating a DDP model. We carefully discuss how to implement the algorithm in practice, and use a simple dynamic store choice model to illustrate how to apply this algorithm to obtain parameter estimates.

Keywords: Bayesian Dynamic Programming; Discrete Choice Dynamic Programming; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 M3 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-dcm and nep-ecm
Date: 2009-04

Downloads: (external link)
http://www.econ.queensu.ca/working_papers/papers/qed_wp_1201.pdf First version 2009 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:qed:wpaper:1201

Access Statistics for this paper

More papers in Working Papers from Queen's University, Department of Economics
Contact information at EDIRC.
Series data maintained by Mark Babcock ().