IDEAS home Printed from https://ideas.repec.org/a/bla/jecrev/v69y2018i3p306-323.html

Identification of Dynamic Models of Rewards Programme

Author

Listed:
  • Andrew T. Ching
  • Masakazu Ishihara

Abstract

“Frequent‐buyer” rewards programmes are commonly used by companies as a marketing tool to compete for market share. They provide a unique environment for studying consumers’ forward‐looking behaviour. The consumer's problem on accumulating reward points can be formulated as a stationary infinite horizon discrete choice dynamic programming model. We show that the parameters of this model, including the discount factor, are well‐identified. In particular, it is possible to identify state‐dependent discount factors (i.e. discount factors can vary with the number of reward points). We discuss how this identification result is related to the goal‐gradient hypothesis studied in the consumer psychology literature.

Suggested Citation

  • Andrew T. Ching & Masakazu Ishihara, 2018. "Identification of Dynamic Models of Rewards Programme," The Japanese Economic Review, Japanese Economic Association, vol. 69(3), pages 306-323, September.
    • Handle: RePEc:bla:jecrev:v:69:y:2018:i:3:p:306-323
    DOI: 10.1111/jere.12188
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jere.12188
    Download Restriction: no
    File URL: https://libkey.io/10.1111/jere.12188?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
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Shuo Zhang & Tat Y. Chan & Xueming Luo & Xiaoyi Wang, 2022. "Time-Inconsistent Preferences and Strategic Self-Control in Digital Content Consumption," Marketing Science, INFORMS, vol. 41(3), pages 616-636, May.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    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:bla:jecrev:v:69:y:2018:i:3:p:306-323. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/jeaaaea.html .

    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.