Hierarchical Bayes Mixed logit modelling for purchase car behaviour

This paper analyses the purchase behaviour for conventional and alternative fuel cars, using Italian stated preference discrete choice data. We propose a flexible Hierarchical Bayesian Mixed Logit (HBML) model that permit us to take into account of possible dependences of the car attribute random parameters on individual characteristics, like age and gender. Moreover, alternative-specific parameters and correlation across alternatives have been easily added to the model. We carried out a survey of the literature on vehicle purchase choice selecting applications of discrete choice models in which a Bayesian approach was adopted. It reveals that our study seems to be the first application of HBML models to analyse this type of stated choices. Moreover, in order to approximate the joint posterior distribution of both the model parameters and hyper-parameters, in this paper we use the most efficient Hamiltonian Monte Carlo sampler, instead of considering the more traditionally Markov Chain Monte Carlo (MCMC) methods as e.g. Gibbs sampler. The modelling results show the usefulness of the proposed method.

CARMECI Gaetano;  VALERI Eva; 

2016-03-09

Edizioni Università di Trieste (EUT)

JRC98621

978-88-8303-718-4,   

http://www.openstarts.units.it/dspace/handle/10077/11881,    https://publications.jrc.ec.europa.eu/repository/handle/JRC98621,