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A Bayesian Cross-Validated Likelihood Method for Comparing Alternative Specifications of Quantitative Models

Author

Listed:
  • Roland T. Rust

    (University of Texas at Austin)

  • David C. Schmittlein

    (The Wharton School, University of Pennsylvania)

Abstract

There are many situations in marketing in which several alternative quantitative models may be built to model a particular marketing phenomenon or system. Few methods exist for comparing the fit of such models if the models are not nested, especially if their performance on each of several criteria is important. This paper proposes a Bayesian cross-validated likelihood (BCVL) method for comparing quantitative models. It can be used when the models are either nested or nonnested, and is especially useful for nonnested models. A simulation based upon a typical marketing modeling situation shows the incremental benefit of using the BCVL method rather than existing techniques, and explores the circumstances under which BCVL works best. The applicability of the BCVL method is demonstrated using several typical marketing modeling situations.

Suggested Citation

  • Roland T. Rust & David C. Schmittlein, 1985. "A Bayesian Cross-Validated Likelihood Method for Comparing Alternative Specifications of Quantitative Models," Marketing Science, INFORMS, vol. 4(1), pages 20-40.
    • Handle: RePEc:inm:ormksc:v:4:y:1985:i:1:p:20-40
    DOI: 10.1287/mksc.4.1.20
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    Cited by:

    1. Erin Anderson, 2008. "The Salesperson as Outside Agent or Employee: A Transaction Cost Analysis," Marketing Science, INFORMS, vol. 27(1), pages 70-84, 01-02.
    2. Namwoon Kim & Jin K. Han & Rajendra K. Srivastava, 2002. "A Dynamic IT Adoption Model for the SOHO Market: PC Generational Decisions with Technological Expectations," Management Science, INFORMS, vol. 48(2), pages 222-240, February.
    3. repec:wyi:journl:002122 is not listed on IDEAS
    4. Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
    5. Abhik Roy, 2022. "A dynamic model of price competition and promotion in prescription drug markets," Marketing Letters, Springer, vol. 33(4), pages 577-591, December.
    6. Lehmann, Donald R., 2020. "The evolving world of research in marketing and the blending of theory and data," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 27-42.
    7. Astrea Camstra & Anne Boomsma, 1992. "Cross-Validation in Regression and Covariance Structure Analysis," Sociological Methods & Research, , vol. 21(1), pages 89-115, August.
    8. Abhik Roy & Jagmohan Raju, 2011. "The influence of demand factors on dynamic competitive pricing strategy: An empirical study," Marketing Letters, Springer, vol. 22(3), pages 259-281, September.
    9. Eric T. Bradlow & David C. Schmittlein, 2000. "The Little Engines That Could: Modeling the Performance of World Wide Web Search Engines," Marketing Science, INFORMS, vol. 19(1), pages 43-62, June.
    10. He A Xu & Alireza Modirshanechi & Marco P Lehmann & Wulfram Gerstner & Michael H Herzog, 2021. "Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-32, June.
    11. Islam, Towhidul & Meade, Nigel, 2000. "Modelling diffusion and replacement," European Journal of Operational Research, Elsevier, vol. 125(3), pages 551-570, September.

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