Nonlinear Time Series Analysis - (Wiley Probability and Statistics) by Ruey S Tsay & Rong Chen (Hardcover)

Book Synopsis

A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis

Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. 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 nonlinear time series analysis, 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 solutions manual and companion website

Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis 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.

From the Back Cover

A COMPREHENSIVE RESOURCE THAT DRAWS A BALANCE BETWEEN THEORY AND APPLICATIONS OF NONLINEAR TIME SERIES ANALYSIS

Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. 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 nonlinear time series analysis, 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, Nonlinear Time Series Analysis 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.

About the Author

RUEY S. TSAY, PHD, is H.G.B. Alexander Professor of Econometrics and Statistics at The University of Chicago Booth School of Business. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics.Dr. Tsay is author of Analysis of Financial Time Series, Multivariate Time Series Analysis, and An Introduction to Analysis of Financial Data with R all published by Wiley.

RONG CHEN, PHD, is Distinguished Professor of Statistics and Director of the Master programs in Financial Statistics and Risk Management and in Data Science at Rutgers University. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics.