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A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models

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  • Andrew Ching
  • Susumu Imai
  • Masakazu Ishihara
  • Neelam Jain

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.
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Suggested Citation

  • Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    • Handle: RePEc:kap:qmktec:v:10:y:2012:i:2:p:151-196
    DOI: 10.1007/s11129-012-9119-6
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    8. 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.
    9. 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.
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    12. Sara Amoroso, 2014. "The hidden costs of R&D collaboration," JRC Working Papers on Corporate R&D and Innovation 2014-02, Joint Research Centre.
    13. Hiroyuki Kasahara & Katsumi Shimotsu, 2018. "Estimation of Discrete Choice Dynamic Programming Models," The Japanese Economic Review, Japanese Economic Association, vol. 69(1), pages 28-58, March.
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    15. 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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    More about this item

    Keywords

    Bayesian estimation; Dynamic programming; Discrete choice models; Rewards programs; C11; C35; C61; D91; M31;
    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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