A Random Effects Multinomial Probit Model of Car Ownership Choice.

by Agostino Nobile, Chandra R. Bhat and Eric I. Pas

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.

Keywords: Multinomial probit model, Panel data, Gibbs sampling, Metropolis algorithm, Bayesian analysis.

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