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Models for Dependent Time Series
Langbeschreibung
This book addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. It shows how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data. The book presents several extensions to the standard autoregressive model and other novel material developed by the authors that has not been published elsewhere. Data sets, MATLAB code, and additional material are available on a supplementary website.
Inhaltsverzeichnis
Introduction and Overview. Lagged Regression and Autoregressive Models. Spectral Analysis of Dependent Series. The Estimation of Vector Autoregressions. Graphical Modeling of Structural VARs. VZAR: An Extension of the VAR Model. Continuous Time VZAR Models. Irregularly Sampled Series. Linking Graphical, Spectral and VZAR Methods. Bibliography. Index.
Granville Tunnicliffe Wilson is a reader emeritus in the Department of Mathematics and Statistics at Lancaster University, UK. His research focuses on methodology and software for time series modeling and prediction.
ISBN-13:
9781420011500
Veröffentl:
2015
Seiten:
340
Autor:
Granville Tunnicliffe Wilson
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
2 - DRM Adobe
Sprache:
Englisch
66,49 €*
Lieferzeit: Sofort lieferbar
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