Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models

Abstract

The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more. To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment. Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data. Differences between both approaches were found as well as similarities not found in earlier studies. Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of time for forecasting and policy evaluation.