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Characterizing interdependencies of multiple time series = theory and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Characterizing interdependencies of multiple time series/ by Yuzo Hosoya ... [et al.].
Reminder of title:	theory and applications /
other author:	Hosoya, Yuzo.
Published:	Singapore :Springer Singapore : : 2017.,
Description:	x, 133 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Time-series analysis. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-6436-4
ISBN:	9789811064364

Characterizing interdependencies of multiple time series = theory and applications /
Characterizing interdependencies of multiple time seriestheory and applications /[electronic resource] :by Yuzo Hosoya ... [et al.]. - Singapore :Springer Singapore :2017. - x, 133 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

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.

ISBN: 9789811064364

Standard No.: 10.1007/978-981-10-6436-4doiSubjects--Topical Terms:

528412
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Characterizing interdependencies of multiple time series = theory and applications /
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505 0 $a 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.
520 $a 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.
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