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Models for dependent time series / Granville Tunnicliffe Wilson, Marco Reale, John Haywood.

By: Tunnicliffe Wilson, Granville [author].
Contributor(s): Reale, Marco [author] | Haywood, John [author].
Material type: TextTextSeries: Monographs on statistics and applied probability ; 142.Publisher: Boca Raton : CRC Press, ©2016Description: xv, 323 p. : illustrations ; 24 cm.ISBN: 9781584886501 (hardcover : alk. paper).Subject(s): Time-series analysis | Autoregression (Statistics) | Mathematical statisticsDDC classification: 000SA.3
Contents:
1: Introduction and overview; 2: Lagged regression and autoregressive models; 3: Spectral analysis of dependent series; 4: Estimation of vector autoregressions; 5: Graphical modeling of structural VARs; 6: VZAR: An extension of the VAR model; 7: Continuous time VZAR models; 8: Irregularly sampled series; 9: Linking graphical, spectral and VZAR methods; References; indexes.
Summary: The book shows 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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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.3 T926 (Browse shelf) Available 136934
Total holds: 0

Includes bibliographical references and indexes.

1: Introduction and overview;
2: Lagged regression and autoregressive models;
3: Spectral analysis of dependent series;
4: Estimation of vector autoregressions;
5: Graphical modeling of structural VARs;
6: VZAR: An extension of the VAR model;
7: Continuous time VZAR models;
8: Irregularly sampled series;
9: Linking graphical, spectral and VZAR methods;
References;
indexes.

The book shows 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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