MNP is a publicly available R package that fits the Bayesian multinomial probit model via
Markov chain Monte Carlo. The multinomial probit model is often used to analyze the …

This research compares several approaches to inference in the multinomial probit model,
based on two Monte Carlo experiments for a seven choice model. The methods compared …

A simulation evaluation is presented to compare alternative estimation techniques for a five-
alternative multinomial probit (MNP) model with random parameters, including cross …

We develop new methods for conducting a finite sample, likelihood-based analysis of the
multinomial probit model. Using a variant of the Gibbs sampler, an algorithm is developed to …

In this chapter, we discuss Bayesian analysis of the multinomial probit model (MNP) using
Markov chain Monte Carlo methods. Although the MNP model has been in the econometrics …

Previous authors have made Bayesian multinomial probit models identifiable by fixing a
parameter on the main diagonal of the covariance matrix. The choice of which element one …

We introduce a set of new Markov chain Monte Carlo algorithms for Bayesian analysis of the
multinomial probit model. Our Bayesian representation of the model places a new, and …

The multinomial probit model is often used to analyze choice behavior. However, estimation
with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which …

The multinomial probit (MNP) model is a primary application for combining simulation with
estimation. Indeed, McFadden (1989) featured the MNP model in his seminal paper. As …

In this paper, we introduce a new flexible mixed model for multinomial discrete choice where
the key individual-and alternative-specific parameters of interest are allowed to follow an …