A Practitioner's Guide To Bayesian Estimation Of Discrete Choice Dynamic Programming Models
Andrew Ching (),
Susumu Imai (),
Masakazu Ishihara and
Neelam Jain ()
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Masakazu Ishihara: University of Toronto
No 1201, Working Paper from Economics Department, Queen's University
This paper provides a step-by-step guide to estimating discrete choice dynamic programming(DDP) models using the Bayesian Dynamic Programming algorithm developedin Imai, Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithmwith the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm,which solves the DDP model and estimates its structural parameters simultaneously. Themain computational advantage of this estimation algorithm is the efficient use of informationobtained 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 thecomputational results obtained from the past iterations to help solving the DDP model atthe current iterated parameter values. Consequently, it significantly alleviates the computationalburden of estimating a DDP model. We carefully discuss how to implementthe algorithm in practice, and use a simple dynamic store choice model to illustrate howto 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)
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Journal Article: A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models (2012)
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