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A Generalized Additive Logit Model of Brand Choice

Journal: Journal of Contemporary Issues in Business Research (Vol.1, No. 3)

Publication Date:

Authors : ;

Page : 69-77

Keywords : Logit model; Generalized additive model; Interactions; Backfitting algorithm; Penalized regression splines.;

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The paper presents an econometric application of generalized additive Logit regression models (GALRMs) for brand choice. Our semi-parametric models are flexible and robust extensions of the Logit model. The GALRMs are fit to binary response data by maximizing a penalized log likelihood or a penalized log partial-likelihood. The GAMs allow us to build a regression surface as a sum of lower-dimensional nonparametric terms circumventing the curse of dimensionality: the slow convergence of an estimator to the true value in high dimensions. Four GALRMs are compared with a Logit model for brand choice and the best model is selected using various model selection criteria.

Last modified: 2015-01-16 05:59:53