The paper compares the empirical performance of two recently suggested techniques for estimating Multinomial Probit (MNP) models. The application concerns the choice of the first practice location of general practitioners in Quebec (Canada). Regional similarities are accounted for by modeling interdependent choice decisions. One technique is a simulated maximum likelihood based approach that relies on a Geweke, Hajivassiliou, and Keane (GHK) choice probability simulator, and the other one exploits the Gibbs sampler with data augmentation. The results indicate that both estimation techniques give similar results. Compared to its competitor, the Gibbs approach is much simpler to implement both conceptually and computationally.
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