PUB - Publikationen an der Universität Bielefeld

Discrete choice models lie at the heart of many transportation models. This holds true for mode choice models as well as for models of vehicle purchase decisions, to name just a few applications. Individual heterogeneity in preferences for different decision makers has been modelled by imposing mixing distributions on the coefficients. However, the literature does not provide much guidance so far for the specification of the mixing distributions apart from trial and error procedures using several standard parametric distributions. We propose an approach that is capable of approximating any underlying mixing distribution. The approach uses a Bayesian framework for estimating a latent class mixed multinomial probit model where the number of latent classes is updated within the algorithm on a weight-based strategy. Presenting simulation results, we demonstrate that the approach is suitable for guiding the specification of mixing distributions in empirical applications.