The number of cars in a household has an important effect on its travel behavior (e.g., choice of number of trips, mode to work and non-work destinations), hence car ownership modeling is an essential component of any travel demand forecasting effort. In this paper we report on a random effects multinomial probit model of car ownership level, estimated using longitudinal data collected in the Netherlands. A Bayesian approach is taken and the model is estimated by means of a modification of the Gibbs sampling with data augmentation algorithm considered by McCulloch and Rossi (1994). The modification consists in performing, after each Gibbs sampling cycle, a Metropolis step along a direction of constant likelihood. An examination of the simulation output illustrates the improved performance of the resulting sampler.

%B Case Studies in Bayesian Statistics %S Lecture Notes in Statistics %I Springer New York %V 121 %P 419-434 %@ 978-0-387-94990-1 %G eng %U http://dx.doi.org/10.1007/978-1-4612-2290-3_13 %R 10.1007/978-1-4612-2290-3_13