Nonlinear Time Series Analysis

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.

Avainsanat:

Guide to nonlinear time series analysis; introduction to nonlinear time series analysis; understanding nonlinear time series analysis; text on nonlinear time series analysis; resource to nonlinear time series analysis; Monte Carlos methods; Markov models; theory of nonlinear statistical methods; applications of nonlinear statistical methods; time series analysis; large data sets and time series analysis; high frequency data and time series analysis; real world time sets; parametric methods of time series analysis; nonparametric methods of time series analysis; Bayesian approach to nonlinear time series analysis; classical approaches to nonlinear time series analysis; advantages of the nonlinear models; limitations of nonlinear models