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Title:
Nonlinear time series : semiparametric and nonparametric methods
Personal Author:
Series:
Monographs on statistics and applied probability ; 108
Publication Information:
Boca Raton, FL : Chapman and Hall/CRC, 2007
Physical Description:
vii, 237 p. : ill. ; 24 cm.
ISBN:
9781584886136

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30000010226341 QA280 G36 2007 Open Access Book
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Summary

Summary

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 Methodsfocuses 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.ndence. 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

Prefacep. v
1 Introductionp. 1
1.1 Preliminariesp. 1
1.2 Examples and modelsp. 1
1.3 Bibliographical notesp. 14
2 Estimation in Nonlinear Time Seriesp. 15
2.1 Introductionp. 15
2.2 Semiparametric series estimationp. 18
2.3 Semiparametric kernel estimationp. 26
2.4 Semiparametric single-index estimationp. 35
2.5 Technical notesp. 39
2.6 Bibliographical notesp. 47
3 Nonlinear Time Series Specificationp. 49
3.1 Introductionp. 49
3.2 Testing for parametric mean modelsp. 50
3.3 Testing for semiparametric variance modelsp. 65
3.4 Testing for other semiparametric modelsp. 68
3.5 Technical notesp. 72
3.6 Bibliographical notesp. 80
4 Model Selection in Nonlinear Time Seriesp. 83
4.1 Introductionp. 83
4.2 Semiparametric cross-validation methodp. 86
4.3 Semiparametric penalty function methodp. 92
4.4 Examples and applicationsp. 95
4.5 Technical notesp. 105
4.6 Bibliographical notesp. 110
5 Continuous-Time Diffusion Modelsp. 111
5.1 Introductionp. 111
5.2 Nonparametric and semiparametric estimationp. 116
5.3 Semiparametric specificationp. 123
5.4 Empirical comparisonsp. 130
5.5 Technical notesp. 146
5.6 Bibliographical notesp. 156
6 Long-Range Dependent Time Seriesp. 157
6.1 Introductory resultsp. 157
6.2 Gaussian semiparametric estimationp. 159
6.3 Simultaneous semiparametric estimationp. 161
6.4 LRD stochastic volatility modelsp. 169
6.5 Technical notesp. 189
6.6 Bibliographical notesp. 191
7 Appendixp. 193
7.1 Technical lemmasp. 193
7.2 Asymptotic normality and expansionsp. 198
Referencesp. 209
Author Indexp. 230
Subject Indexp. 235