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Alternative Computational Approaches to Inference in the Multinomial Probit Model

John Geweke, Michael Keane and David Runkle
The Review of Economics and Statistics
Vol. 76, No. 4 (Nov., 1994), pp. 609-632
Published by: The MIT Press
DOI: 10.2307/2109766
Stable URL: http://www.jstor.org/stable/2109766
Page Count: 24
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Alternative Computational Approaches to Inference in the Multinomial Probit Model
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Abstract

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 are the simulated maximum likelihood estimator using the GHK recursive probability simulator, the method of simulated moments estimator using the GHK recursive simulator and kernel-smoothed frequency simulators, and posterior means using a Gibbs sampling-data augmentation algorithm. Overall, the Gibbs sampling algorithm has a slight edge, with the relative performance of MSM and SML based on the GHK simulator being difficult to evaluate. The MSM estimator with the kernel-smoothed frequency simulator is clearly inferior.

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