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Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling

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Listed:
  • Makoto Abe

    (Faculty of Economics, University of Tokyo)

  • Yasemin Boztug

    (Institute of Marketing, Humboldt University of Berlin)

  • Lutz Hildebrandt

    (Institute of Marketing, Humboldt University of Berlin)

Abstract

The Multinomial Logit (MNL) model is still the only viable option to study nonlinear responsiveness of utility to covariates nonparametrically. This research investigates whether MNL structure of inter-brand competition is a reasonable assumption, so that when the utility function is estimated nonparametrically, the IIA assumption does not bias the result. For this purpose, the authors compare the performance of two comparable nonpara-metric choice models that differ in one aspect: one assumes MNL com-petitive structure and the other infers the pattern of brands' competition nonparametrically from data.

Suggested Citation

  • Makoto Abe & Yasemin Boztug & Lutz Hildebrandt, 2003. "Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling," CIRJE F-Series CIRJE-F-193, CIRJE, Faculty of Economics, University of Tokyo.
  • Handle: RePEc:tky:fseres:2003cf193
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    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2003/2003cf193.pdf
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    References listed on IDEAS

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    1. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    2. Hausman, Jerry & McFadden, Daniel, 1984. "Specification Tests for the Multinomial Logit Model," Econometrica, Econometric Society, vol. 52(5), pages 1219-1240, September.
    3. Abe, Makoto, 1999. "A Generalized Additive Model for Discrete-Choice Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 271-284, July.
    4. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    5. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    6. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    7. Makoto Abe, 1995. "A Nonparametric Density Estimation Method for Brand Choice Using Scanner Data," Marketing Science, INFORMS, vol. 14(3), pages 300-325.
    8. Füsun Gönül & Kannan Srinivasan, 1993. "Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues," Marketing Science, INFORMS, vol. 12(3), pages 213-229.
    9. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    10. Briesch R.A. & Chintagunta P.K. & Matzkin R.L., 2002. "Semiparametric Estimation of Brand Choice Behavior," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 973-982, December.
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