offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models.

The need for this book is based on the recent developments in, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide:

• Offers research developed by leading scholars of time series analysis
• Presents R commands making it possible to reproduce all the analyses included in the text
• Contains real-world examples throughout the book
• Recommends exercises to test understanding of material presented
• Includes an instructor-only solutions manual on a Wiley Book Companion Site, and data sets hosted by the authors

Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series,offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.

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