Long-Memory Time Series: Theory and Methods
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:
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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Chapter 1 Stationary Processes
Chapter 2 State Space Systems
Chapter 3 LongMemory Processes
Chapter 4 Estimation Methods
Chapter 5 Asymptotic Theory
Chapter 6 Heteroskedastic Models
Chapter 7 Transformations
Chapter 8 Bayesian Methods
Chapter 9 Prediction