Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling
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.
|Date of creation:||Feb 2003|
|Contact details of provider:|| Postal: Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033|
Web page: http://www.cirje.e.u-tokyo.ac.jp/index.html
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Makoto Abe, 1995. "A Nonparametric Density Estimation Method for Brand Choice Using Scanner Data," Marketing Science, INFORMS, vol. 14(3), pages 300-325.
- 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.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:tky:fseres:2003cf193. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CIRJE administrative office)
If references are entirely missing, you can add them using this form.