Alternative Computational Approaches to Statistical Inference In The Multinomial Probit Model

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dc.contributor.author	Geweke John	en_US
dc.contributor.author	Keane Michael	en_US
dc.contributor.author	Runkle D	en_US
dc.contributor.editor		en_US
dc.date.accessioned	2011-02-07T06:25:49Z
dc.date.available	2011-02-07T06:25:49Z
dc.date.issued	1994	en_US
dc.identifier	2006012700	en_US
dc.identifier.citation	Geweke John, Keane Michael, and Runkle D 1994, 'Alternative Computational Approaches to Statistical Inference In The Multinomial Probit Model', Mit Press, vol. 76, no. 4, pp. 609-632.	en_US
dc.identifier.issn	0034-6535	en_US
dc.identifier.other	C1	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 probabilit	en_US
dc.language		en_US
dc.publisher	Mit Press	en_US
dc.relation.isbasedon	NA	en_US
dc.title	Alternative Computational Approaches to Statistical Inference In The Multinomial Probit Model	en_US
dc.parent	Review Of Economics And Statistics	en_US
dc.journal.volume	76	en_US
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	en_US
dc.personcode	101228;998871;0000033773	en_US
dc.percentage	000100	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
dc.staffid	University of Minnesota	en_US