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dc.contributor.authorHuang, JJen_US
dc.contributor.authorTzeng, GHen_US
dc.contributor.authorOng, CSen_US
dc.date.accessioned2014-12-08T15:16:50Z-
dc.date.available2014-12-08T15:16:50Z-
dc.date.issued2006-04-15en_US
dc.identifier.issn0096-3003en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.amc.2005.08.032en_US
dc.identifier.urihttp://hdl.handle.net/11536/12374-
dc.description.abstractIn this paper, a dynamic factor model is proposed to extract the dynamic factors from time series data. In order to deal with the problem of scaling, the cross-correlation matrices (CCM) are first employed to cluster the time series data. Then, the dynamic factors are extracted using the revised independent component analysis (ICA). In addition, a numerical study is used to demonstrate the proposed method. On the basis of the simulated results, we can conclude that the proposed method can really extract the effective dynamic factors. (c) 2005 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectdynamic factor modelen_US
dc.subjectfactor analysisen_US
dc.subjectcross-correlation matrices (CCM)en_US
dc.subjectindependent component analysis (ICA)en_US
dc.subjecttime seriesen_US
dc.titleA novel algorithm for dynamic factor analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.amc.2005.08.032en_US
dc.identifier.journalAPPLIED MATHEMATICS AND COMPUTATIONen_US
dc.citation.volume175en_US
dc.citation.issue2en_US
dc.citation.spage1288en_US
dc.citation.epage1297en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000237568000028-
dc.citation.woscount4-
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