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Alternative Computational Approaches to Inference in the Multinomial Probit Model

Staff Report 170 | Published May 1, 1994

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Authors

David E. Runkle Senior Economist

John F. Geweke

Michael P. Keane

Alternative Computational Approaches to Inference in the Multinomial Probit Model

Abstract

This research compares several approaches to inference in the multinomial probit model, based on Monte-Carlo results for a seven choice model. The experiment compares 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. Each estimator is applied in nine different models, which have from 1 to 40 free parameters. The performance of all estimators is found to be satisfactory. However, the results indicate that the method of simulated moments estimator with the kernel-smoothed frequency simulator does not perform quite as well as the other three methods. Among those three, the Gibbs sampling-data augmentation algorithm appears to have a slight overall edge, with the relative performance of MSM and SML based on the GHK simulator difficult to determine.


Published in: _Review of Economics and Statistics_ (Vol. 76, No. 4, November 1994, pp. 609-632) https://doi.org/10.2307/2109766.