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.
1. Stationary Processes.
2. State Space Systems.
3. Long–Memory Processes.
4. Estimation Methods.
5. Asymptotic Theory.
6. Heteroskedastic Models.
8. Bayesian Methods.
11. Missing Data.