Characterizing interdependencies of multiple time series : theory and applications /
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| Imprint: | Singapore : Springer, [2017] |
|---|---|
| Description: | 1 online resource (x, 133 pages) |
| Language: | English |
| Series: | SpringerBriefs in statistics, JSS Research series in statistics, 2191-544X SpringerBriefs in statistics. JSS research series in statistics. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11384629 |
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| 245 | 0 | 0 | |a Characterizing interdependencies of multiple time series : |b theory and applications / |c Yuzo Hosoya, Kosuke Oya, Taro Takimoto, Ryo Kinoshita. |
| 264 | 1 | |a Singapore : |b Springer, |c [2017] | |
| 300 | |a 1 online resource (x, 133 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a SpringerBriefs in statistics, JSS Research series in statistics, |x 2191-544X | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed November 2, 2017). | |
| 505 | 0 | |a Preface -- Acknowledgements -- Contents -- 1 Introduction -- 1.1 On Empirical Causality -- 1.2 Causality in Economic Analysis -- 1.3 Empirical Economic Models -- 1.3.1 The Cowles Approach -- 1.3.2 Economic Time-Series Models -- 1.4 Basic Concepts for Statistical Inference -- 1.4.1 Conditional Inference -- 1.4.2 Defining Exogeneity -- 1.4.3 Interpretative Problems -- References -- 2 The Measures of One-Way Effect, Reciprocity, and Association -- 2.1 Prediction and Causality -- 2.1.1 Statement of the Problem -- 2.1.2 Terminology and Notations | |
| 505 | 8 | |a 2.2 Defining Non-causality2.3 The One-Way Effect Measure -- 2.4 Alternative Methods for Deriving Mv tou(λ) -- 2.4.1 Distributed-Lag Representation Approach -- 2.4.2 Innovation Orthogonalization Approach -- 2.5 Measures of Association and Reciprocity -- 2.6 Examples -- References -- 3 Representation of the Partial Measures -- 3.1 Introduction -- 3.2 Third-Series Involvement -- 3.3 Partial Measures of Interdependence -- 3.3.1 Representing the Partial Measures -- 3.3.2 Glossary on Partial Measures of Interdependence -- 3.3.3 The Stationary ARMA Model | |
| 505 | 8 | |a 3.4 Extension to Non-stationary Reproducible ProcessesReferences -- 4 Inference Based on the Vector Autoregressive and Moving Average Model -- 4.1 Inference Procedure -- 4.1.1 Three-Step Estimation Procedure -- 4.1.2 Optimization Algorithm in Step 3 -- 4.1.3 Monte Carlo Wald Test of Measures of Interdependence -- 4.1.4 Monte Carlo Wald Testing of Non-causality -- 4.2 Simulation Performance -- 4.2.1 Designing Monte Carlo Simulation -- 4.2.2 Simulation Results -- 4.2.3 Comparison of Step 2 and Step 3 Estimation -- 4.3 Empirical Analysis of Macroeconomic Series | |
| 505 | 8 | |a 4.3.1 Literature4.3.2 Application of the Partial Measures to US Macroeconomic Data -- References -- 5 Inference on Changes in Interdependence Measures -- 5.1 Change in Measures -- 5.1.1 Change in Measures for Stationary Vector ARMA Model -- 5.1.2 Inference for Noncausal Relationship -- 5.2 Tests Based on Subsampling Method -- 5.2.1 Test for a Change in Measures Using High-Frequency Data -- 5.2.2 Variance Estimation via Subsampling -- 5.3 A Simulation Study of Finite Sample Test Properties -- 5.3.1 Change in Simple Causality Measure | |
| 505 | 8 | |a 5.3.2 Change in Partial Causality Measure5.4 Empirical Illustrations -- 5.4.1 Stock Returns and Dividend Yields -- 5.4.2 Intra-Daily Financial Time Series -- References -- Appendix Technical Supplements | |
| 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.-- |c Provided by publisher. | ||
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Time-series analysis. |2 fast |0 (OCoLC)fst01151190 | |
| 655 | 0 | |a Electronic book. | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Hosoya, Yuzo, |e author. |0 http://id.loc.gov/authorities/names/no2018002408 | |
| 700 | 1 | |a Oya, Kosuke, |e author. |0 http://id.loc.gov/authorities/names/no2018002268 | |
| 700 | 1 | |a Takimoto, Taro, |e author. |0 http://id.loc.gov/authorities/names/no2018002461 | |
| 700 | 1 | |a Kinoshita, Ryo, |e author. |0 http://id.loc.gov/authorities/names/no2018002107 | |
| 776 | 0 | 8 | |i Printed edition: |z 9789811064357 |
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