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Long-Memory Time Series: Theory and Methods

ISBN: 978-0-470-13145-9
304 pages
April 2007
Long-Memory Time Series: Theory and Methods (0470131454) cover image
A self-contained, contemporary treatment of the analysis of long-range dependent data

Long-Memory Time Series: Theory and Methods provides an overview of the theory and methods developed to deal with long-range dependent data and describes the applications of these methodologies to real-life time series. Systematically organized, it begins with the foundational essentials, proceeds to the analysis of methodological aspects (Estimation Methods, Asymptotic Theory, Heteroskedastic Models, Transformations, Bayesian Methods, and Prediction), and then extends these techniques to more complex data structures.

To facilitate understanding, the book:

  • Assumes a basic knowledge of calculus and linear algebra and explains the more advanced statistical and mathematical concepts

  • Features numerous examples that accelerate understanding and illustrate various consequences of the theoretical results

  • Proves all theoretical results (theorems, lemmas, corollaries, etc.) or refers readers to resources with further demonstration

  • Includes detailed analyses of computational aspects related to the implementation of the methodologies described, including algorithm efficiency, arithmetic complexity, CPU times, and more

  • Includes proposed problems at the end of each chapter to help readers solidify their understanding and practice their skills

A valuable real-world reference for researchers and practitioners in time series analysis, economerics, finance, and related fields, this book is also excellent for a beginning graduate-level course in long-memory processes or as a supplemental textbook for those studying advanced statistics, mathematics, economics, finance, engineering, or physics. A companion Web site is available for readers to access the S-Plus and R data sets used within the text.

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Preface.

Acronyms.

1. Stationary Processes.

2. State Space Systems.

3. Long-Memory Processes.

4. Estimation Methods.

5. Asymptotic Theory.

6. Heteroskedastic Models.

7. Transformations.

8. Bayesian Methods.

9. Prediction.

10. Regression.

11. Missing Data.

12. Seasonality.

References.

Topic Index.

Author Index.

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Wilfredo Palma, PhD, is Chairman and Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. Dr. Palma has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics.
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"...Palma presents a textbook for a graduate course summarizing the theory and methods developed to deal with long-range-dependent data, and describing some applications to real-life time series." (SciTech Book Reviews, June 2007)

"...textbook for a graduate course summarizing the theory and methods developed to deal with long-range-dependent data, and describing some applications to real-life time series.... Problems and bibliographic notes are provided at the end of each chapter." (SciTech Book News, June 2007)

"I believe that this text provides an important contribution to the long-memory time series literature. I feel that it largely achieves its aims and could be useful for those instructors wishing to teach a semester-long special topics course.... I strongly recommend this book to anyone interested in long-memory time series. Both researchers and beginners alike will find this text extremely useful." (Journal of the American Statisticial Association, Dec 2008)

"Very well-organized catalogue of long-memory time series analysis." (Mathematical Reviews, 2008)

"Judging by its contents and scope [the aim of this book] has been largely achieved.... The list of references is selective but quite comprehensive. Each chapter concludes with a 'Problems' section which should be helpful to instructors wishing to use this book as standalone basis for a course in its subject area..." (International Statistical Review, 2007)

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