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Investigating the competitive assumption of Multinomial Logit models of brand choice by nonparametric modeling

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Author Info

  • Makoto Abe
  • Yasemin Boztug
  • Lutz Hildebrandt

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.

(This abstract was borrowed from another version of this item.)

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File URL: http://hdl.handle.net/10.1007/BF02753916
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Bibliographic Info

Article provided by Springer in its journal Computational Statistics.

Volume (Year): 19 (2004)
Issue (Month): 4 (December)
Pages: 635-657

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Handle: RePEc:spr:compst:v:19:y:2004:i:4:p:635-657

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Web page: http://www.springerlink.com/link.asp?id=120306

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Related research

Keywords: nonparametric method; generalized additive models; brand choice; IIA; multinomial logit model; scanner panel data;

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References

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  1. Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. D. McFadden & J. Hausman, 1981. "Specification Tests for the Multinominal Logit Model," Working papers 292, Massachusetts Institute of Technology (MIT), Department of Economics.
  3. repec:wop:humbsf:1996-88 is not listed on IDEAS
  4. Abe, Makoto, 1999. "A Generalized Additive Model for Discrete-Choice Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 271-84, July.
  5. Makoto Abe, 1995. "A Nonparametric Density Estimation Method for Brand Choice Using Scanner Data," Marketing Science, INFORMS, vol. 14(3), pages 300-325.
  6. 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.
  7. 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.
  8. 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.
  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. 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.
  11. 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.
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