Bayes Estimation of Latent Class Mixed Multinomial Probit Models

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. The standard random utility models often fail to take into account heterogeneity of individual deciders, compare for example Train (1), Chapter 6, or Train (2). Individual heterogeneity in preferences for different deciders 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. Based on the ideas of Train (2) and Bhat and Lavieri (3) recently Bauer et al. (4) introduced procedures for non-parametrically estimating mixed multinomial probit models. While these procedures have been demonstrated to be useful in cross-sectional context, the evaluation of the probit likelihood in the panel setting proofs numerically challenging. Extending Scaccia and Marcucci (5) the authors propose an approach that on the one hand is capable of approximating the underlying mixing distribution while on the other hand being numerically favorable since no likelihood function has to be evaluated. 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, the authors demonstrate that the approach is suitable for guiding the specification of mixing distributions in empirical applications.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 13p

Subject/Index Terms

Filing Info

  • Accession Number: 01764118
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-00715
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:20AM