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題名 Trimmed Granger Causality Between Two Groups of Time Series
作者 Hung,Ying-Chao;Tseng,Neng-Fang;Narayanaswamy, Balakrishnan
貢獻者 統計系
日期 2014-10
上傳時間 23-Dec-2014 15:08:36 (UTC+8)
摘要 The identification of causal effects between two groups of time series has been an important topic in a wide range of applications such as economics, engineering, medicine, neuroscience, and biology. In this paper, a simplified causal relationship (called trimmed Granger causality) based on the context of Granger causality and vector autoregressive (VAR) model is introduced. The idea is to characterize a subset of “important variables” for both groups of time series so that the underlying causal structure can be presented based on minimum variable information. When the VAR model is specified, explicit solutions are provided for the identification of important variables. When the parameters of the VAR model are unknown, an efficient statistical hypothesis testing procedure is introduced to estimate the solution. An example representing the stock indices of different countries is used to illustrate the proposed methods. In addition, a simulation study shows that the proposed methods significantly outperform the Lasso-type methods in terms of the accuracy of characterizing the simplified causal relationship.
關聯 Electronic Journal of Statistics,8( 2) ,1940-1972
資料類型 article
DOI http://dx.doi.org/10.1214/14-EJS940
dc.contributor 統計系en_US
dc.creator (作者) Hung,Ying-Chao;Tseng,Neng-Fang;Narayanaswamy, Balakrishnanen_US
dc.date (日期) 2014-10en_US
dc.date.accessioned 23-Dec-2014 15:08:36 (UTC+8)-
dc.date.available 23-Dec-2014 15:08:36 (UTC+8)-
dc.date.issued (上傳時間) 23-Dec-2014 15:08:36 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/72215-
dc.description.abstract (摘要) The identification of causal effects between two groups of time series has been an important topic in a wide range of applications such as economics, engineering, medicine, neuroscience, and biology. In this paper, a simplified causal relationship (called trimmed Granger causality) based on the context of Granger causality and vector autoregressive (VAR) model is introduced. The idea is to characterize a subset of “important variables” for both groups of time series so that the underlying causal structure can be presented based on minimum variable information. When the VAR model is specified, explicit solutions are provided for the identification of important variables. When the parameters of the VAR model are unknown, an efficient statistical hypothesis testing procedure is introduced to estimate the solution. An example representing the stock indices of different countries is used to illustrate the proposed methods. In addition, a simulation study shows that the proposed methods significantly outperform the Lasso-type methods in terms of the accuracy of characterizing the simplified causal relationship.en_US
dc.format.extent 411328 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Electronic Journal of Statistics,8( 2) ,1940-1972en_US
dc.title (題名) Trimmed Granger Causality Between Two Groups of Time Seriesen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1214/14-EJS940en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1214/14-EJS940en_US