Gao Nonlinear Time Series

Semiparametric and Nonparametric Methods
Erscheinungsjahr 2010
ISBN: 978-1-4200-1121-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 237 Seiten

Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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.

Zielgruppe

Researchers and graduate students in statistics, mathematics, econometrics, and finance.

Autoren/Hrsg.

Gao, Jiti

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

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