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Characterizing interdependencies of multiple time series [electronic resource] : theory and applications

Characterizing interdependencies of multiple time series [electronic resource] : theory and applications

자료유형
E-Book(소장)
개인저자
Hosoya, Yuzo.
서명 / 저자사항
Characterizing interdependencies of multiple time series [electronic resource] : theory and applications / Yuzo Hosoya ... [et al.].
발행사항
Singapore :   Springer,   c2017.  
형태사항
1 online resource (x, 133 p.) : ill.
총서사항
SpringerBriefs in statistics, JSS Research series in statistics,2191-544X
ISBN
9789811064357 9789811064364 (eBook)
요약
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.
일반주기
Title from e-Book title page.  
내용주기
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.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Time-series analysis.
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245 0 0 ▼a Characterizing interdependencies of multiple time series ▼h [electronic resource] : ▼b theory and applications / ▼c Yuzo Hosoya ... [et al.].
260 ▼a Singapore : ▼b Springer, ▼c c2017.
300 ▼a 1 online resource (x, 133 p.) : ▼b ill.
490 1 ▼a SpringerBriefs in statistics, JSS Research series in statistics, ▼x 2191-544X
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Time-series analysis.
700 1 ▼a Hosoya, Yuzo.
830 0 ▼a SpringerBriefs in statistics, JSS Research series in statistics.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-981-10-6436-4
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 519.55 등록번호 E14014979 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

컨텐츠정보

목차

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.

관련분야 신착자료

Bertsimas, Dimitris (2022)
Doane, David P (2022)