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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 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.

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 Paper 1201, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1201
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    2. Andrew T. Ching & Masakazu Ishihara, 2018. "Identification of Dynamic Models of Rewards Programme," The Japanese Economic Review, Springer, vol. 69(3), pages 306-323, September.
    3. Jeremy Schwartz, 2019. "The Job Search Intensity Supply Curve: How Labor Market Conditions Affect Job Search Effort," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 45(2), pages 269-300, April.
    4. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    5. Andrew T. Ching & Matthew Osborne, 2020. "Identification and Estimation of Forward-Looking Behavior: The Case of Consumer Stockpiling," Marketing Science, INFORMS, vol. 39(4), pages 707-726, July.
    6. 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.
    7. 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.
    8. Hiroyuki Kasahara & Katsumi Shimotsu, 2018. "Estimation of Discrete Choice Dynamic Programming Models," The Japanese Economic Review, Springer, vol. 69(1), pages 28-58, March.
    9. Murasawa, Yasutomo, 2023. "大学中退の逐次意思決定モデルの構造推定 [Structural estimation of a sequential decision model of college dropout]," MPRA Paper 118183, University Library of Munich, Germany.
    10. Masakazu Ishihara & Andrew T. Ching, 2019. "Dynamic Demand for New and Used Durable Goods Without Physical Depreciation: The Case of Japanese Video Games," Marketing Science, INFORMS, vol. 38(3), pages 392-416, May.
    11. Shunyuan Zhang & Param Vir Singh & Anindya Ghose, 2019. "A Structural Analysis of the Role of Superstars in Crowdsourcing Contests," Service Science, INFORMS, vol. 30(1), pages 15-33, March.
    12. 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.
    13. 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.
    14. Sara Amoroso, 2014. "The hidden costs of R&D collaboration," JRC Working Papers on Corporate R&D and Innovation 2014-02, Joint Research Centre.
    15. Sun, Yutec & Ishihara, Masakazu, 2019. "A computationally efficient fixed point approach to dynamic structural demand estimation," Journal of Econometrics, Elsevier, vol. 208(2), pages 563-584.
    16. Chul Kim & P. K. Kannan & Michael Trusov & Andrea Ordanini, 2020. "Modeling Dynamics in Crowdfunding," Marketing Science, INFORMS, vol. 39(2), pages 339-365, March.

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    More about this item

    Keywords

    Bayesian Dynamic Programming; Discrete Choice Dynamic Programming; Markov Chain Monte Carlo;
    All these keywords.

    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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