Alternative computational approaches to inference in the multinomial probit model

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dc.contributor.author Geweke, J
dc.contributor.author Keane, M
dc.contributor.author Runkle, D
dc.date.accessioned 2011-02-07T06:25:49Z
dc.date.issued 1994-11
dc.identifier.citation Review of Economics and Statistics, 1994, 76 (4), pp. 609 - 632
dc.identifier.issn 0034-6535
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/13896
dc.description.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. © 1994.
dc.language eng
dc.title Alternative computational approaches to inference in the multinomial probit model
dc.type Journal Article
dc.parent Review of Economics and Statistics
dc.journal.volume 4
dc.journal.volume 76
dc.journal.number 4 en_US
dc.publocation Cambridge en_US
dc.identifier.startpage 609 en_US
dc.identifier.endpage 632 en_US
dc.cauo.name BUS.School of Finance and Economics en_US
dc.conference Verified OK en_US
dc.for 140302 Econometric and Statistical Methods
dc.personcode 998871
dc.personcode 101228
dc.percentage 100 en_US
dc.classification.name Econometric and Statistical Methods en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Estimability; Integration; Simulation en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Business
pubs.organisational-group /University of Technology Sydney/Faculty of Business/Economics


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