IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v10y2012i2p151-196.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.
(This abstract was borrowed from another version of this item.)

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11129-012-9119-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11129-012-9119-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Andriy Norets, 2010. "Continuity and differentiability of expected value functions in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 1(2), pages 305-322, November.
    2. Baohong Sun, 2005. "Promotion Effect on Endogenous Consumption," Marketing Science, INFORMS, vol. 24(3), pages 430-443, July.
    3. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    4. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    5. Botao Yang & Andrew T. Ching, 2014. "Dynamics of Consumer Adoption of Financial Innovation: The Case of ATM Cards," Management Science, INFORMS, vol. 60(4), pages 903-922, April.
    6. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
    7. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    8. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    9. 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.
    10. Daniel Diermeier & Michael Keane & Antonio Merlo, 2005. "A Political Economy Model of Congressional Careers," American Economic Review, American Economic Association, vol. 95(1), pages 347-373, March.
    11. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
    12. Andrew T. Ching, 2010. "A Dynamic Oligopoly Structural Model For The Prescription Drug Market After Patent Expiration," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(4), pages 1175-1207, November.
    13. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    14. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    15. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    16. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    17. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 497-529.
    18. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-672, November.
    19. John Rust, 1997. "Using Randomization to Break the Curse of Dimensionality," Econometrica, Econometric Society, vol. 65(3), pages 487-516, May.
    20. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    21. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    22. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, August.
    23. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
    24. Lancaster, Tony, 1997. "Exact Structural Inference in Optimal Job-Search Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 165-179, April.
    25. Günter J. Hitsch, 2006. "An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty," Marketing Science, INFORMS, vol. 25(1), pages 25-50, 01-02.
    26. Wesley Hartmann, 2006. "Intertemporal effects of consumption and their implications for demand elasticity estimates," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 325-349, December.
    27. Susumu Imai & Kala Krishna, 2004. "Employment, Deterrence, And Crime In A Dynamic Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(3), pages 845-872, August.
    28. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    29. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    30. Inseong Song & Pradeep Chintagunta, 2003. "A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category," Quantitative Marketing and Economics (QME), Springer, vol. 1(4), pages 371-407, December.
    31. Andriy Norets, 2009. "Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables," Econometrica, Econometric Society, vol. 77(5), pages 1665-1682, September.
    32. Füsun Gönül & Kannan Srinivasan, 1996. "Estimating the Impact of Consumer Expectations of Coupons on Purchase Behavior: A Dynamic Structural Model," Marketing Science, INFORMS, vol. 15(3), pages 262-279.
    33. Mariano,Roberto & Schuermann,Til & Weeks,Melvyn J. (ed.), 2000. "Simulation-based Inference in Econometrics," Cambridge Books, Cambridge University Press, number 9780521591126, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shervin Shahrokhi Tehrani & Andrew T. Ching, 2024. "A Heuristic Approach to Explore: The Value of Perfect Information," Management Science, INFORMS, vol. 70(5), pages 3200-3224, May.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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.
    10. 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.
    11. 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.
    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.
    14. 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.
    15. Chul Kim & P. K. Kannan & Michael Trusov & Andrea Ordanini, 2020. "Modeling Dynamics in Crowdfunding," Marketing Science, INFORMS, vol. 39(2), pages 339-365, March.
    16. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    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. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    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. 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.
    6. Brett R. Gordon, 2009. "A Dynamic Model of Consumer Replacement Cycles in the PC Processor Industry," Marketing Science, INFORMS, vol. 28(5), pages 846-867, 09-10.
    7. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    8. Panle Jia Barwick & Parag A. Pathak, 2015. "The costs of free entry: an empirical study of real estate agents in Greater Boston," RAND Journal of Economics, RAND Corporation, vol. 46(1), pages 103-145, March.
    9. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
    10. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.
    11. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    12. 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.
    13. Yufeng Huang, 2019. "Learning by Doing and the Demand for Advanced Products," Marketing Science, INFORMS, vol. 38(1), pages 107-128, January.
    14. 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.
    15. Wesley Hartmann, 2006. "Intertemporal effects of consumption and their implications for demand elasticity estimates," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 325-349, December.
    16. Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.
    17. Andriy Norets, 2009. "Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables," Econometrica, Econometric Society, vol. 77(5), pages 1665-1682, September.
    18. Kalouptsidi, Myrto & Scott, Paul & Souza-Rodrigues, Edouardo, 2015. "Identification of Counterfactuals and Payoffs in Dynamic Discrete Choice with an Application to Land Use," TSE Working Papers 15-596, Toulouse School of Economics (TSE).
    19. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    20. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:qmktec:v:10:y:2012:i:2:p:151-196. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.