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

Author

Listed:
  • 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.

Suggested Citation

  • Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2009. "A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models," Working Papers 1201, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1201
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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1201.pdf
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    References listed on IDEAS

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    Cited by:

    1. Février, Philippe & Wilner, Lionel, 2016. "Do consumers correctly expect price reductions? Testing dynamic behavior," International Journal of Industrial Organization, Elsevier, vol. 44(C), pages 25-40.
    2. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Other publications TiSEM f5784a49-7053-401d-855d-1, Tilburg University, School of Economics and Management.
    3. Andrew Ching & Masakazu Ishihara, 2014. "Dynamic Demand for New and Used Durable Goods without Physical Depreciation: The Case of Japanese Video Games," 2014 Meeting Papers 782, Society for Economic Dynamics.
    4. 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.
    5. 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.
    6. 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.
    7. Sara Amoroso, 2014. "The hidden costs of R&D collaboration," JRC Working Papers on Corporate R&D and Innovation 2014-02, Joint Research Centre (Seville site).

    More about this item

    Keywords

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

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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