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An Ideal-Point Probabilistic Choice Model for Heterogeneous Preferences

Author

Listed:
  • Wagner A. Kamakura

    (State University of New York at Buffalo)

  • Rajendra K. Srivastava

    (University of Texas)

Abstract

This paper presents a new ideal point probabilistic choice model. Unlike the model suggested by Cooper and Nakanishi (Cooper, L. G., M. Nakanishi. 1983. Two logit models for external analysis of preferences. (4) 607–619.) which attempts to capture choices via a single ideal point, the proposed model, though based on aggregate data, allows for heterogeneity in preferences by estimating a distribution of ideal points. The model accounts for substitutability among choice alternatives and alleviates one of the major sources for the violation of the “Independence from Irrelevant Alternatives” property. It is demonstrated that the final form of the model is a Multinomial Probit, with a covariance matrix that depends on the relative position of the choice alternatives. An empirical application is provided and the resulting parameters are compared to the distributions of ideal points and attribute weights obtained via LINMAP (at the individual level) and via both the Logit and Probit versions of the model proposed by Cooper and Nakanishi (at the aggregate level).

Suggested Citation

  • Wagner A. Kamakura & Rajendra K. Srivastava, 1986. "An Ideal-Point Probabilistic Choice Model for Heterogeneous Preferences," Marketing Science, INFORMS, vol. 5(3), pages 199-218.
    • Handle: RePEc:inm:ormksc:v:5:y:1986:i:3:p:199-218
    DOI: 10.1287/mksc.5.3.199
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    Citations

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    Cited by:

    1. Davies, Antony & Cline, Thomas W., 2005. "A consumer behavior approach to modeling monopolistic competition," Journal of Economic Psychology, Elsevier, vol. 26(6), pages 797-826, December.
    2. Bordley, Robert F., 2011. "An anti-ideal point representation of economic discrete choice models," Economics Letters, Elsevier, vol. 110(1), pages 60-63, January.
    3. Roy, Abhik, 1998. "An error components approach to segmentation and modelling brand choice dynamics," Journal of Economic Psychology, Elsevier, vol. 19(4), pages 463-484, August.
    4. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    5. Dan Horsky & Paul Nelson, 2006. "Testing the Statistical Significance of Linear Programming Estimators," Management Science, INFORMS, vol. 52(1), pages 128-135, January.
    6. Jan R. Landwehr & Aparna A. Labroo & Andreas Herrmann, 2011. "Gut Liking for the Ordinary: Incorporating Design Fluency Improves Automobile Sales Forecasts," Marketing Science, INFORMS, vol. 30(3), pages 416-429, 05-06.
    7. Baier, Daniel & Gaul, Wolfgang, 1998. "Optimal product positioning based on paired comparison data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 365-392, November.
    8. Ronald Goettler & Ron Shachar, 2000. "Estimating Product Characteristics and Spatial Competition in the Network Television Industry," Econometric Society World Congress 2000 Contributed Papers 1691, Econometric Society.
    9. Wagner Kamakura, 1991. "Estimating flexible distributions of ideal-points with external analysis of preferences," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 419-431, September.

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