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Hardcover: 237 pages
Publisher:Chapman Hall/CRC; 1 edition (March 22, 2007)
Language: English
Book Description
Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully nonparametric models and methods. Answering the call for an up-to-date overview of the latest developments in the field, Nonlinear Time Series: Semiparametric and Nonparametric Methods focuses on various semiparametric methods in model estimation, specification testing, and selection of time series data. After a brief introduction, the book examines semiparametric estimation and specification methods and then applies these approaches to a class of nonlinear continuous-time models with real-world data. It also assesses some newly proposed semiparametric estimation procedures for time series data with long-range dependence. Even though the book only deals with climatological and financial data, the estimation and specifications methods discussed can be applied to models with real-world data in many disciplines. This resource covers key methods in time series analysis and provides the necessary theoretical details. The latest applied finance and financial econometrics results and applications presented in the book enable researchers and graduate students to keep abreast of developments 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 note
Bibliographical notes
MODEL SELECTION IN NONLINEAR TIME SERIES
Introduction
Semiparametric cross-validation method
Semiparametric penalty function method
Examples and applications
Technical notes
Bibliographical notes
CONTINUOUS-TIME DIFFUSION MODELS
Introduction
Nonparametric and semiparametric estimation
Semiparametric specification
Empirical comparisons
Technical notes
Bibliographical notes
LONG-RANGE DEPENDENT TIME SERIES
Introductory results
Gaussian semiparametric estimation
Simultaneous semiparametric estimation
LRD stochastic volatility models
Technical notes
Bibliographical notes
APPENDIX
Technical lemmas
Asymptotic normality and expansions
REFERENCES
AUTHOR INDEX
SUBJECT INDEX
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