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＜電子ブック＞
Characterizing Interdependencies of Multiple Time Series : Theory and Applications / by Yuzo Hosoya, Kosuke Oya, Taro Takimoto, Ryo Kinoshita
(JSS Research Series in Statistics. ISSN:23640065)

版	1st ed. 2017.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2017
大きさ	X, 133 p. 32 illus : online resource
著者標目	*Hosoya, Yuzo author Oya, Kosuke author Takimoto, Taro author Kinoshita, Ryo author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Biometry LCSH:Social sciences—Statistical methods LCSH:Mathematical statistics—Data processing FREE:Statistical Theory and Methods FREE:Biostatistics FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistics and Computing FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	1: Introduction to statistical causal analysis -- 2: Measures of one-way effect, reciprocity and association -- 3: Partial measures of interdependence -- 4: Inference based on the vector autoregressive and moving average model -- 5: Inference on change in causality measures -- 6: Simulation performance of estimation methods -- 7: Empirical analysis of macroeconomic series -- 8: Empirical analysis of change in causality measures -- 9: Conclusion -- Appendix -- References -- Index 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 the statistical inferences of these measures are presented, with a focus on the stationary multivariate autoregressive moving-average processes, which include the estimation and test of causality change. Empirical analyses are provided to illustrate what alternative aspects are detected and how the methods introduced here can be conveniently applied. Most of the materials in Chapters 4 and 5 are based on the authors' latest research work. Subsidiary items are collected in the Appendix HTTP:URL=https://doi.org/10.1007/978-981-10-6436-4

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