Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/67780
Authors: 
Ching, Andrew
Imai, Susumu
Ishihara, Masakazu
Jain, Neelam
Year of Publication: 
2009
Series/Report no.: 
Queen's Economics Department Working Paper 1201
Abstract: 
This paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed in 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.
Subjects: 
Bayesian Dynamic Programming
Discrete Choice Dynamic Programming
Markov Chain Monte Carlo
JEL: 
C11
M03
Document Type: 
Working Paper

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