Models for Dependent Time Series

Models for Dependent Time Series

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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. Web Resource A supplementary website provides the data sets used in the examples as well as documented MATLAB(R) 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 more

Product details

  • Hardback | 340 pages
  • 163 x 311 x 25mm | 646g
  • Taylor & Francis Inc
  • CRC Press Inc
  • Bosa Roca, United States
  • English
  • 149 black & white illustrations, 21 black & white tables
  • 1584886501
  • 9781584886501
  • 2,429,028

Review quote

"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 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 ( 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 other text on multivariate time series and contains a lot of modern material not found elsewhere. Chapters 1-4 take a look at the historical treatment of multivariate time series, not dwelling on theory, but concentrating on applications and intuitive motivation. The remaining chapters comprise work done mainly by the authors in the last 20 years, introducing and integrating concepts, such as graphical modeling, using directed acyclic graphs and a vector version of the ZAR models, which they have invented, developed, and applied. There are also chapters on continuous time and irregularly sampled time series. Throughout, the accent is on application, and the book is thus suitable for a broader audience than existing, more theoretical texts. Indeed, the book should be accessible to anyone modeling multivariate time series. MATLAB code and other explanations are to be made available to complement the text." -Barry Quinn, Professor of Statistics, Macquarie University, Australiashow more

About Granville Tunnicliffe Wilson

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 more

Table of contents

Introduction and overview Examples of time series Dependence within and between time series Some of the challenges of time series modeling Feedback and cycles Challenges of high frequency sampling Causal modeling and structure Some practical considerations Lagged regression and autoregressive models Stationary discrete time series and correlation Autoregressive approximation of time series Multi-step autoregressive model prediction Examples of autoregressive model approximation The multivariate autoregressive model Autoregressions for high lead time prediction Model impulse response functions The covariances of the VAR model Partial correlations of the VAR model Inverse covariance of the VAR model Autoregressive Moving Average models State space representation of VAR models Projection using the covariance matrix Lagged response functions of the VAR model Spectral analysis of dependent series Harmonic components of time series Cycles and lags Cycles and stationarity The spectrum and cross-spectra of time series Dependence between harmonic components Bivariate and multivariate spectral properties Estimation of spectral properties Sample covariances and smoothed spectrum Tapering and pre-whitening Practical examples of spectral analysis Harmonic contrasts in large samples The estimation of vector autoregressions Methods of estimation The spectrum of a VAR model Yule-Walker estimation of the VAR(p) model Estimation of the VAR(p) by lagged regression Maximum likelihood estimation, MLE VAR models with exogenous variables, VARX The Whittle likelihood of a time series model Graphical modeling of structural VARs The structural VAR, SVAR The directed acyclic graph, DAG The conditional independence graph, CIG Interpretation of CIGs Properties of CIGs Estimation and selection of DAGs Building a structural VAR, SVAR Properties of partial correlation graphs Simultaneous equation modeling An SVAR model for the Pig market: the innovations A full SVAR model of the Pig market series VZAR: an extension of the VAR model Discounting the past The generalized shift operator The VZAR model Properties of the VZAR model Approximating a process by the VZAR model Yule-Walker fitting of the VZAR Regression fitting of the VZAR Maximum likelihood fitting of the VZAR VZAR model assessment Continuous time VZAR models Continuous time series Continuous time autoregression and the CAR(1) The CAR(p) model The continuous time generalized shift The continuous time VZAR model, VCZAR Properties of the VCZAR model Approximating a continuous process by a VCZAR Yule-Walker fitting of the VCZAR model Regression and ML estimation of the VCZAR Irregularly sampled series Modeling of irregularly sampled series The likelihood from irregularly sampled data Irregularly sampled univariate series models The spectrum of irregularly sampled series Recommendations on VCZAR model selection A model of regularly sampled bivariate series A model of irregularly sampled bivariate series Linking graphical, spectral and VZAR methods Outline of topics Partial coherency graphs Spectral estimation of causal responses The structural VZAR, SVZAR Further possible developments Bibliography Subject Index Author Indexshow more