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Cited by 338

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Alternative Computational Approaches to Inference in the Multinomial Probit Model

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Geweke, John, Keane, Michael, Runkle, David·1994·The Review of Economics and Statistics

Abstract

Abstract-This research compares several approaches to in-ference 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 simu-lator and kernel-smoothed frequency simulators, and poste-rior means using a Gibbs sampling-data augmentation algo-rithm. 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. I.

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Metadata

Authors
Geweke, J., Keane, M., & Runkle, D.
Venue
The Review of Economics and Statistics
Publisher
JSTOR
Published
1994-11
Volume
76
Issue
4
Pages
609
ISSN
0034-6535 (ISSN-L)
OpenAlex
W3021862730
Cited by
338
References
1
FWCI
22.50
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Alternative Computational Approaches to Inference in the Multinomial Probit Model. https://app.cochat.ai/papers/10.2307/2109766
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Geweke, J., Keane, M., & Runkle, D. (1994). Alternative Computational Approaches to Inference in the Multinomial Probit Model. The Review of Economics and Statistics. https://doi.org/10.2307/2109766

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