Nonlinear Time Series: Semiparametric and Nonparametric Methods (Monographs on Statistics and Applied Probability)
Hardcover: 237 pages Publisher: Chapman & Hall/CRC; 1 edition (March 22, 2007) Language: English Book Description
Usefulin the theoretical and empirical analysis of nonlinear time seriesdata, semiparametric methods have received extensive attention in theeconomics and statistics communities over the past twenty years. Recentstudies show that semiparametric methods and models may be applied tosolve dimensionality reduction problems arising from using fullynonparametric models and methods. Answering the call for an up-to-dateoverview of the latest developments in the field, Nonlinear TimeSeries: Semiparametric and Nonparametric Methods focuses on varioussemiparametric methods in model estimation, specification testing, andselection of time series data. After a brief introduction, the bookexamines semiparametric estimation and specification methods and thenapplies these approaches to a class of nonlinear continuous-time modelswith real-world data. It also assesses some newly proposedsemiparametric estimation procedures for time series data withlong-range dependence. Even though the book only deals withclimatological and financial data, the estimation and specificationsmethods discussed can be applied to models with real-world data in manydisciplines. This resource covers key methods in time series analysisand provides the necessary theoretical details. The latest appliedfinance and financial econometrics results and applications presentedin the book enable researchers and graduate students to keep abreast ofdevelopments in the field.
Table of Contents
INTRODUCTION
Preliminaries
Examples and models
Bibliographic notes
ESTIMATION IN NONLINEAR TIME SERIES
Introduction
Semiparametric series estimation
Semiparametric kernel estimation
Semiparametric single-index estimation
Technical notes
Bibliographical notes
NONLINEAR TIME SERIES SPECIFICATION
Introduction
Testing for parametric mean models
Testing for semiparametric variance models
Testing for other semiparametric models
Technical notes
Bibliographical notes
MODEL SELECTION IN NONLINEAR TIME SERIES
Introduction
Semiparametric cross-validation method
Semiparametric penalty function method
Examples and applications
Technical notes
Bibliographical notes
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