Nonlinear Time Series: Nonparametric and Parametric Methods

by Jianqing Fan, Qiwei Yao

This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.

576 pages, Kindle Edition

First published January 1, 2003

Reviews

[Medhat2 · Rating 2 out of 5]

This book offers a rare and balanced perspective that equally values both parametric precision and nonparametric flexibility, making it a must-read for anyone venturing into nonlinear dynamics in time series data.
ARMA Modeling & forecasting
Parametric Nonlinear Time Series Models
Nonparametric Density Estimation
Smoothing in Time Series
Spectral Density Estimation and Its Application
Nonparametric Models
Model Validation
Nonlinear Prediction