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Models for dependent time series

Author: Granville Tunnicliffe-Wilson; Marco Reale; John Haywood, (Mathematics professor)
Publisher: Boca Raton : Taylor & Francis, 2015.
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data. The first four chapters discuss the two main  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Tunnicliffe-Wilson, Granville.
Models for dependent time series.
(DLC) 2015014849
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Granville Tunnicliffe-Wilson; Marco Reale; John Haywood, (Mathematics professor)
ISBN: 1420011502 9781420011500
OCLC Number: 921132572
Notes: "A CRC title."
Description: 1 online resource.
Contents: Cover; Contents; Preface; Chapter 1: Introduction and overview; Chapter 2: Lagged regression and autoregressive models; Chapter 3: Spectral analysis of dependent series; Chapter 4: Estimation of vector autoregressions; Chapter 5: Graphical modeling of structural VARs; Chapter 6: VZAR: An extension of the VAR model; Chapter 7: Continuous time VZAR models; Chapter 8: Irregularly sampled series; Chapter 9: Linking graphical, spectral and VZAR methods; References.
Series Title: Chapman & Hall/CRC Monographs on Statistics & Applied Probability.
Responsibility: Granville Tunnicliffe-Wilson, Marco Reale, John Haywood.

Abstract:

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data. The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater.

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"This book covers the three important pillars of multiple time series-vector autoregressive modeling, spectral analysis, and graphical models-a useful characteristic for a modern book on time series Read more...

 
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