A fast estimation procedure for discrete choice random coefficients demand model

Kim, DH; Song, Y; Xu, H

A fast estimation procedure for discrete choice random coefficients demand model

Kim, DH Song, Y Xu, H

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Publication Type:: Journal Article
Citation:: Applied Economics, 2017, 49 (58), pp. 5849 - 5855
Issue Date:: 2017-12-14

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Field	Value	Language
dc.contributor.author	Kim, DH	en_US
dc.contributor.author	Song, Y	en_US
dc.contributor.author	Xu, H	en_US
dc.date.issued	2017-12-14	en_US
dc.identifier.citation	Applied Economics, 2017, 49 (58), pp. 5849 - 5855	en_US
dc.identifier.issn	0003-6846	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127195
dc.description.abstract	© 2017 Informa UK Limited, trading as Taylor & Francis Group. We document speed-up gains of graphical processing unit (GPU) computing over central processing unit (CPU) for the estimation of discrete choice random coefficient demand model. When we use a moderate-sized GPU, the computation is six to twenty times faster, where the smallest speed-up factor, six, is obtained from a comparison with the parallel computing over sixteen CPU cores.	en_US
dc.relation.ispartof	Applied Economics	en_US
dc.relation.isbasedon	10.1080/00036846.2017.1349289	en_US
dc.subject.classification	Economics	en_US
dc.title	A fast estimation procedure for discrete choice random coefficients demand model	en_US
dc.type	Journal Article
utslib.citation.volume	58	en_US
utslib.citation.volume	49	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
utslib.for	1117 Public Health and Health Services	en_US
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.issue	58	en_US
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

© 2017 Informa UK Limited, trading as Taylor & Francis Group. We document speed-up gains of graphical processing unit (GPU) computing over central processing unit (CPU) for the estimation of discrete choice random coefficient demand model. When we use a moderate-sized GPU, the computation is six to twenty times faster, where the smallest speed-up factor, six, is obtained from a comparison with the parallel computing over sixteen CPU cores.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127195