A Generalized Additive Model for Discrete-Choice Data

Bibliographic Data

ID19417499
AuthorsMakoto Abe (0000-0003-4441-6414, The University of Tokyo, corresponding author)
Year1999
Volume17
Issue3
Pages271-284
Publication date1999-07-01
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.1999.10524817
OpenAlexW2057604028
LanguageEN
Citations received5
References cited23

The usual assumption of a linear-in-parameters utility function in a multinomial logit model is relaxed by a sum of one-dimensional nonparametric functions of the explanatory variables. The model generalizes the logistic regression of the generalized additive model for a binary response to a qualitative variable that can assume more than two values. Simulation studies show that the proposed method can recover underlying nonlinearity in utility of various shapes. The model is applied to consumer panel data collected by bar-code scanners from two product categories, and the marketing implications are sought

Additive model · Discrete choice · Econometrics · Generalized additive model · Generalized linear model · Logistic regression · Logit · Mixed logit · Multinomial logistic regression · Nonparametric statistics · Ordered logit · Statistics · Variables · Consumer Market Behavior and Pricing · Economic and Environmental Valuation · Mathematics · Wine Industry and Tourism · Applied Mathematics

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