A novel algorithm for dynamic factor analysis
| dc.citation.epage | 1297 | en_US |
| dc.citation.issue | 2 | en_US |
| dc.citation.spage | 1288 | en_US |
| dc.citation.volume | 175 | en_US |
| dc.citation.woscount | 4 | |
| dc.contributor.author | Huang, JJ | en_US |
| dc.contributor.author | Tzeng, GH | en_US |
| dc.contributor.author | Ong, CS | en_US |
| dc.contributor.department | 科技管理研究所 | zh_TW |
| dc.contributor.department | Institute of Management of Technology | en_US |
| dc.date.accessioned | 2014-12-08T15:16:50Z | |
| dc.date.available | 2014-12-08T15:16:50Z | |
| dc.date.issued | 2006-04-15 | en_US |
| dc.description.abstract | In 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.identifier.doi | 10.1016/j.amc.2005.08.032 | en_US |
| dc.identifier.issn | 0096-3003 | en_US |
| dc.identifier.journal | APPLIED MATHEMATICS AND COMPUTATION | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.amc.2005.08.032 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/12374 | |
| dc.identifier.wosnumber | WOS:000237568000028 | |
| dc.language.iso | en_US | en_US |
| dc.subject | dynamic factor model | en_US |
| dc.subject | factor analysis | en_US |
| dc.subject | cross-correlation matrices (CCM) | en_US |
| dc.subject | independent component analysis (ICA) | en_US |
| dc.subject | time series | en_US |
| dc.title | A novel algorithm for dynamic factor analysis | en_US |
| dc.type | Article | en_US |