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Identification of Dynamic Models of Rewards Programme

Author

Listed:
  • Andrew T. Ching

    (University of Toronto)

  • Masakazu Ishihara

    (New York University)

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, Springer, vol. 69(3), pages 306-323, September.
    • Handle: RePEc:spr:jecrev:v:69:y:2018:i:3:d:10.1111_jere.12188
    DOI: 10.1111/jere.12188
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    References listed on IDEAS

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    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. 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.
    3. 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.
    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.

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    More about this item

    Keywords

    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
    • 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

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