During the last decades long-memory processes have evolved as a vital and important part of time series analysis. This book attempts to give an overview of the theory and methods developed to deal with long-range dependent data as well as describe some applications of these methodologies to real-life time series. The topics are systematically organized in a progressive manner, starting from foundations (the first three chapters), progressing to the analysis of methodological implications (the next six chapters), and finally extending to more complex long-range dependent data structures (the final three chapters).

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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