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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. A distinct feature of this book is that it applies many modern nonparametric estimation and testing ideas to time series modeling and model identification, while outlines many useful ideas from more traditional time series analysis. This will enable readers to use modern data-analytic techniques while keeping in touch with traditional approaches, and make the book self-contained. 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.
List of contents
From the contents:
Introduction.- Stationary Time Series.- Smoothing in Time Series.- ARMA Modeling and Forecasting.- Parametric Nonlinear Time Series Models.-Nonparametric Models.- Hypothesis Testing.- Continuous Time Models in Finance.- Nonlinear Prediction.
Summary
This work integrates parametric and nonparametric techniques with time series modelling and prediction, the two important goals of time series analysis. It should benefit researchers and practitioners such as econometricians, meteorologists and biologists, as well as serving as a reference for graduate students in statistics and econometrics.
