A novel logit-type discrete choice model is presented whose distinctive characteristic is that it "polarizes" or forces the prediction of choice probabilities towards values of 0 or 1. In real-world empirical tests this property enabled the new formulation, which we call the polarized logit model (PLM), to outperform the predictive capacity of other classical discrete choice models. The PLM is derived from the optimality conditions of a maximum entropy optimization model with linear and quadratic constraints. These conditions yield a fixed-point logit probability function that exhibits endogeneity, which is corrected for using instrumental variables so that the model's parameters can be estimated. The PLM's marginal substitution rates are similar to those of the traditional logit models. (c) 2013 Elsevier Ltd. All rights reserved.

The authors are grateful for funding provided by the Centro de Desarrollo Urban Sustentable (CEDEUS), Conicyt/Fondap/15110020 and FONDEF Project No. D10I1049.

Centro de Desarrollo Urban Sustentable (CEDEUS); FONDEF [D10I1049]; Conicyt/Fondap [15110020]

louis.degrange@udp.cl; felipe.gonzalezr@udp.cl; ignacio.vargaso@udp.cl; jcm@ing.puc.cl

FONDEF

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