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

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Author Info

  • Andrew Ching

    ()
    (University of Toronto)

  • Susumu Imai

    ()
    (Queen's University)

  • Masakazu Ishihara

    ()
    (University of Toronto)

  • Neelam Jain

    ()
    (Northern Illinois University)

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.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1201.pdf
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1201.

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Length: 49 pages
Date of creation: Apr 2009
Date of revision:
Handle: RePEc:qed:wpaper:1201

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Keywords: Bayesian Dynamic Programming; Discrete Choice Dynamic Programming; Markov Chain Monte Carlo;

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Citations

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Cited by:
  1. Jeremy Schwartz, 2014. "The Job Search Intensity Supply Curve: How Labor Market Conditions Affect Job Search Effort," Upjohn Working Papers and Journal Articles 14-215, W.E. Upjohn Institute for Employment Research.
  2. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
  3. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Open Access publications from Tilburg University urn:nbn:nl:ui:12-5663713, Tilburg University.
  4. Zhou, Yiyi, 2012. "Failure to Launch in Two-Sided Markets: A Study of the U.S. Video Game Market," MPRA Paper 42002, University Library of Munich, Germany.

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