Nonlinear Time Series: Semiparametric and Nonparametric Methods, 1st Edition (Hardback) book cover

Nonlinear Time Series

Semiparametric and Nonparametric Methods, 1st Edition

By Jiti Gao

Chapman and Hall/CRC

237 pages | 34 B/W Illus.

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Hardback: 9781584886136
pub: 2007-03-22
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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.

Reviews

"…The author has presented the material very carefully …There are plenty of real examples and all the methods are illustrated. … I believe the book is extremely useful and definitely will be helpful to many advanced research workers."

Journal of Time Series Analysis, 2009

"The monograph provides a timely addition to the subject of nonlinear time series … the author presents a thorough and rigorous theoretical framework for semiparametric nonlinear time series and analysis."

—Scott H. Holan, University of Missouri-Columbia, Journal of the American Statistical Association, June 2009, Vol. 104, No. 486

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

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

About the Series

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

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Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General