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 material for the remaining chapters, which cover the construction of structural models and the extension of vector autoregressive modeling to high frequency, continuously recorded, and irregularly sampled series. The final chapter combines these approaches with spectral methods for identifying causal dependence between time series.
A supplementary website provides the data sets used in the examples as well as documented MATLAB® functions and other code for analyzing the examples and producing the illustrations. The site also offers technical details on the estimation theory and methods and the implementation of the models.
Table of Contents
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.
Marco Reale is an associate professor in the School of Mathematics and Statistics at the University of Canterbury, New Zealand. His research interests include time series analysis, statistical learning, and stochastic optimization.
John Haywood is a senior lecturer in the School of Mathematics and Statistics at Victoria University of Wellington, New Zealand. His research interests include time series analysis, seasonal modeling, and statistical applications, particularly in ecology.
"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 since each brings new insights to the analyses and each has the ability to complement the other. The book is well-written and should be accessible to anyone with a good understanding of multiple linear regression…the authors are successful in communicating concepts central to modeling time series in the time and frequency domain as well as using the graphical modeling approach. The numerous examples used to illustrate techniques covered in the chapters are easy to follow and this makes the book very useful…The choice of content for the chapters as well as the references for topics covered in the book is excellent…it is a valuable addition to the literature on time series analysis."
—Swati Chandna, University College London, The American Statistician, November 2016
"This book is a valuable contribution to researchers and students working with time series with emphasis on multivariate time series including both the time domain and frequency domain approaches. The presentation is accessible to students with intermediate undergraduate level courses in regression analysis and time series analysis. There is an emphasis on basic principles with many unique insightful approaches such as the introduction of frequency domain thinking using harmonic contrasts and many other such insights…this book contains a wealth of fascinating multivariate time series ranging from applications in finance, economics, management science, ecology, manufacturing, climate change and biology. The authors provide a website (http://www.dependenttimeseries.com) where data and computer software can be downloaded or contributed by interested researchers."
—Journal of Time Series Analysis, June 2016
"I enjoyed reading this book. It is like no othe