Sequential and simultaneous estimation of hybrid discrete choice models: some new findings

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, where the latent variables are built before their integration with the traditional explanatory variables in the choice model, and the simultaneous approach, where both processes are done together, albeit using a sophisticated but fairly complex treatment. In this paper, both approaches are applied to estimate hybrid choice models using two datasets; one coming 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 on unbiased estimators, problems arise when obtaining valuations such as the value of time for forecasting and policy evaluation.