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Identification of Dynamic Models of Rewards Programme

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  • 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
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    References listed on IDEAS

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    Cited by:

    1. 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.
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    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.

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    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

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