Nonlinear Time Series Nonparametric Parametric Methods (Photocopy Edition)

Author: Jianqing Fan et al.
Publisher:
Publish Date: 2006-01-17
Features: This book discusses contemporary statistical methods and nonlinear time series analysis, focusing on nonparametric and semiparametric techniques developed over the past decade. The main contents include modeling techniques in phase space, frequency domain, and time domain; to illustrate the integration of parametric and nonparametric methods in time series data analysis, the book provides new discussions on certain parametric nonlinear models, such as ARCH/GARCH models and threshold models; as well as a concise perspective on ARMA models. The book consistently uses data from real-world applications to demonstrate how nonparametric methods can reveal the local structure of high-dimensional data. It also introduces several important technical tools. This book is suitable for graduate students, practitioners in time series analysis, and researchers with varying levels of expertise in the field. It holds value in the statistical community and in other broad areas such as econometrics, empirical finance, population biology, and ecology. Reading this book requires basic knowledge of probability theory and statistics.