| 书目名称 | Characterizing Interdependencies of Multiple Time Series |
| 副标题 | Theory and Applicati |
| 编辑 | Yuzo Hosoya,Kosuke Oya,Ryo Kinoshita |
| 视频video | |
| 概述 | Presents an approach to characterizing the interdependencies of multivariate time series by means of the basic concept of the one-way effect.Shows how the third-series effect is eliminated with least |
| 丛书名称 | SpringerBriefs in Statistics |
| 图书封面 |  |
| 描述 | .This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement..Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case..Chapters 2 and 3 of the book introduce an improved version of the basic concepts for measuring the one-way effect, reciprocity, and association of multiple time series, which were originally proposed by Hosoya. Then th |
| 出版日期 | Book 2017 |
| 关键词 | Autoregressive Moving-average Model; Canonical Factorization; Causal Analysis; Large Sample Test; Predic |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-10-6436-4 |
| isbn_softcover | 978-981-10-6435-7 |
| isbn_ebook | 978-981-10-6436-4Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s) 2017 |
发表于 2025-3-21 17:12:01
