Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.     

A Simple Method of Incorporating Income Effects into Logit and Nested-Logit Models: Theory and Application

Substantive income effects are incorporated in a logit or nested-logit model by assuming that utility is a piece-wise linear spline function of residual income. Specific income data are not required, only income by category. Expected compensating variation is easily and accurately approximated by the difference between expected maximum utility in the proposed and initial state, multiplied by the inverse of the individual's initial marginal utility of money. This approximation is almost exact because although any policy can, in theory, cause an individual to jump income categories, for most policies this probability will be very small.