File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A Litterman BVAR approach for production forecasting of technology industries

TitleA Litterman BVAR approach for production forecasting of technology industries
Authors
KeywordsAutoregression (AR)
Bayesian vector autoregression (BVAR)
Industrial clusters
Production forecasting
Vector autoregression (VAR)
Issue Date2003
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/techfore
Citation
Technological Forecasting And Social Change, 2003, v. 70 n. 1, p. 67-82 How to Cite?
AbstractForecasting the production of technology industries is important to entrepreneurs and governments, but usually suffers from market fluctuation and explosion. This paper aims to propose a Litterman Bayesian vector autoregression (LBVAR) model for production prediction based on the interaction of industrial clusters. Related industries within industrial clusters are included into the LBVAR model to provide more accurate predictions. The LBVAR model possesses the superiority of Bayesian statistics in small sample forecasting and holds the dynamic property of the vector autoregression (VAR) model. Two technology industries in Taiwan, the photonics industry and semiconductor industry are used to examine the LBVAR model using a rolling forecasting procedure. As a result, the LBVAR model was found to be capable of providing outstanding predictions for these two technology industries in comparison to the autoregression (AR) model and VAR model. © 2002 Elsevier Science Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/141774
ISSN
2015 Impact Factor: 2.678
2015 SCImago Journal Rankings: 1.348
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHsu, PHen_HK
dc.contributor.authorWang, CHen_HK
dc.contributor.authorShyu, JZen_HK
dc.contributor.authorYu, HCen_HK
dc.date.accessioned2011-09-27T03:00:43Z-
dc.date.available2011-09-27T03:00:43Z-
dc.date.issued2003en_HK
dc.identifier.citationTechnological Forecasting And Social Change, 2003, v. 70 n. 1, p. 67-82en_HK
dc.identifier.issn0040-1625en_HK
dc.identifier.urihttp://hdl.handle.net/10722/141774-
dc.description.abstractForecasting the production of technology industries is important to entrepreneurs and governments, but usually suffers from market fluctuation and explosion. This paper aims to propose a Litterman Bayesian vector autoregression (LBVAR) model for production prediction based on the interaction of industrial clusters. Related industries within industrial clusters are included into the LBVAR model to provide more accurate predictions. The LBVAR model possesses the superiority of Bayesian statistics in small sample forecasting and holds the dynamic property of the vector autoregression (VAR) model. Two technology industries in Taiwan, the photonics industry and semiconductor industry are used to examine the LBVAR model using a rolling forecasting procedure. As a result, the LBVAR model was found to be capable of providing outstanding predictions for these two technology industries in comparison to the autoregression (AR) model and VAR model. © 2002 Elsevier Science Inc. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/techforeen_HK
dc.relation.ispartofTechnological Forecasting and Social Changeen_HK
dc.subjectAutoregression (AR)en_HK
dc.subjectBayesian vector autoregression (BVAR)en_HK
dc.subjectIndustrial clustersen_HK
dc.subjectProduction forecastingen_HK
dc.subjectVector autoregression (VAR)en_HK
dc.titleA Litterman BVAR approach for production forecasting of technology industriesen_HK
dc.typeArticleen_HK
dc.identifier.emailHsu, PH: paulhsu@hku.hken_HK
dc.identifier.authorityHsu, PH=rp01553en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/S0040-1625(01)00142-1en_HK
dc.identifier.scopuseid_2-s2.0-0037210599en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037210599&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume70en_HK
dc.identifier.issue1en_HK
dc.identifier.spage67en_HK
dc.identifier.epage82en_HK
dc.identifier.isiWOS:000180210200004-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridHsu, PH=8974031100en_HK
dc.identifier.scopusauthoridWang, CH=8947241600en_HK
dc.identifier.scopusauthoridShyu, JZ=7103007765en_HK
dc.identifier.scopusauthoridYu, HC=7405852085en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats