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Article: Production forecasting of Taiwan's technology industrial cluster: A bayesian autoregression approach

TitleProduction forecasting of Taiwan's technology industrial cluster: A bayesian autoregression approach
Authors
KeywordsBayesian vector autoregression
Forecasting
Industrial clusters
Taiwan
Vector autoregression
Issue Date2005
PublisherJohn Wiley & Sons Ltd.. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/114269012
Citation
Canadian Journal Of Administrative Sciences, 2005, v. 22 n. 2, p. 168-183 How to Cite?
AbstractThis study proposes a forecasting method that combines the clustering effect and non-informative diffuse-prior Bayesian vector autoregression (NDBVAR) model to forecast the productions of technology industries. Two empirical cases are examined to verify the proposed method: the semiconductor industry and computer manufacturing industry in Taiwan. It is found that the NDBVAR model outperforms the other three conventional time series models including the autoregression (AR), vector autoregression (VAR), and Litterman Bayesian VAR (LBVAR) models. Moreover, the NDBVAR model also outperforms the forecast reports from leading market information providers over the past several years. The forecasting method proposed is therefore concluded to be a feasible approach for production prediction, especially for technology industries in volatile environments. © ASAC 2005.
Persistent Identifierhttp://hdl.handle.net/10722/141772
ISSN
2015 Impact Factor: 0.405
2015 SCImago Journal Rankings: 0.271
References

 

DC FieldValueLanguage
dc.contributor.authorLee, JCen_HK
dc.contributor.authorWang, CHen_HK
dc.contributor.authorHsu, PHen_HK
dc.contributor.authorLai, HCen_HK
dc.date.accessioned2011-09-27T03:00:43Z-
dc.date.available2011-09-27T03:00:43Z-
dc.date.issued2005en_HK
dc.identifier.citationCanadian Journal Of Administrative Sciences, 2005, v. 22 n. 2, p. 168-183en_HK
dc.identifier.issn0825-0383en_HK
dc.identifier.urihttp://hdl.handle.net/10722/141772-
dc.description.abstractThis study proposes a forecasting method that combines the clustering effect and non-informative diffuse-prior Bayesian vector autoregression (NDBVAR) model to forecast the productions of technology industries. Two empirical cases are examined to verify the proposed method: the semiconductor industry and computer manufacturing industry in Taiwan. It is found that the NDBVAR model outperforms the other three conventional time series models including the autoregression (AR), vector autoregression (VAR), and Litterman Bayesian VAR (LBVAR) models. Moreover, the NDBVAR model also outperforms the forecast reports from leading market information providers over the past several years. The forecasting method proposed is therefore concluded to be a feasible approach for production prediction, especially for technology industries in volatile environments. © ASAC 2005.en_HK
dc.languageengen_US
dc.publisherJohn Wiley & Sons Ltd.. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/114269012en_HK
dc.relation.ispartofCanadian Journal of Administrative Sciencesen_HK
dc.subjectBayesian vector autoregressionen_HK
dc.subjectForecastingen_HK
dc.subjectIndustrial clustersen_HK
dc.subjectTaiwanen_HK
dc.subjectVector autoregressionen_HK
dc.titleProduction forecasting of Taiwan's technology industrial cluster: A bayesian autoregression approachen_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.doi10.1111/j.1936-4490.2005.tb00716.x-
dc.identifier.scopuseid_2-s2.0-25144467998en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-25144467998&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume22en_HK
dc.identifier.issue2en_HK
dc.identifier.spage168en_HK
dc.identifier.epage183en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLee, JC=37960900100en_HK
dc.identifier.scopusauthoridWang, CH=8947241600en_HK
dc.identifier.scopusauthoridHsu, PH=8974031100en_HK
dc.identifier.scopusauthoridLai, HC=8947241800en_HK

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