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## Details

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:

## Reviews

*Editorial reviews*

Publisher Synopsis

"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 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, Australia Read more...

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