Detection of Common Long-Range Dependence Component for Multivariate Time Series
碩士 === 東海大學 === 統計學系 === 87 === In this paper, we proposed a new method to identifying common long-range dependent component in a multivariate time series. A common long-range dependent component exists if individual series are all long-range dependent but there exists a particular linear combinatio...
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ndltd-TW-087THU003370012016-06-13T04:16:04Z http://ndltd.ncl.edu.tw/handle/43264190534525540518 Detection of Common Long-Range Dependence Component for Multivariate Time Series 多變量時間數列之共同長期相關成份的檢測 Ying-Shen Chen 陳英森 碩士 東海大學 統計學系 87 In this paper, we proposed a new method to identifying common long-range dependent component in a multivariate time series. A common long-range dependent component exists if individual series are all long-range dependent but there exists a particular linear combination of the process which does not have the long-memory property. We first find the linear combination by the two-stage least squares procedure and then test the long-memory property for the transformed data using the method proposed by Geweke and Porter-Hudak (1983). The performance of the proposed test is investigated via Monte Carlo simulation and compared with the previous method based on the canonical correlation analysis. The new approach has better identifying ability than the previous method in detecting common long-range dependence component for multivariate time series. Moreover, it also produces the accurate estimation for the factor loading matrix. For illustration, the squared returns of the daily weighted stock indices are used for three electronic companies in Taiwan--the Acer Incorporated Company, the Compal Electronic Incorporated Company and the Taiwan Semiconductor Manufacturing Company. Nan-Jung Hsu 徐南蓉 1999 學位論文 ; thesis 25 en_US |
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碩士 === 東海大學 === 統計學系 === 87 === In this paper, we proposed a new method to identifying common long-range dependent component in a multivariate time series. A common long-range dependent component exists if individual series are all long-range dependent but there exists a particular linear combination of the process which does
not have the long-memory property. We first find the linear combination by the two-stage least squares procedure and then test the long-memory property for the transformed data using the method proposed by Geweke and Porter-Hudak (1983). The performance of the proposed test is investigated via Monte Carlo simulation and compared with the previous method based on
the canonical correlation analysis. The new approach has better identifying ability than the previous method in detecting common long-range dependence component for multivariate time series. Moreover, it also produces the accurate estimation for the factor loading matrix. For illustration, the squared returns of the daily weighted stock indices are used for three electronic companies in Taiwan--the Acer Incorporated Company, the Compal Electronic Incorporated Company and the Taiwan Semiconductor Manufacturing Company.
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Nan-Jung Hsu |
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Nan-Jung Hsu Ying-Shen Chen 陳英森 |
author |
Ying-Shen Chen 陳英森 |
spellingShingle |
Ying-Shen Chen 陳英森 Detection of Common Long-Range Dependence Component for Multivariate Time Series |
author_sort |
Ying-Shen Chen |
title |
Detection of Common Long-Range Dependence Component for Multivariate Time Series |
title_short |
Detection of Common Long-Range Dependence Component for Multivariate Time Series |
title_full |
Detection of Common Long-Range Dependence Component for Multivariate Time Series |
title_fullStr |
Detection of Common Long-Range Dependence Component for Multivariate Time Series |
title_full_unstemmed |
Detection of Common Long-Range Dependence Component for Multivariate Time Series |
title_sort |
detection of common long-range dependence component for multivariate time series |
publishDate |
1999 |
url |
http://ndltd.ncl.edu.tw/handle/43264190534525540518 |
work_keys_str_mv |
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