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Nonlinear time series: semiparametric and nonparametric methods

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  • Gao, Jiti
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Abstract

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, this 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 this 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 this book enable researchers and graduate students to keep abreast of developments in the field.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 39563.

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Date of creation: 02 Sep 2007
Date of revision: 01 Sep 2007
Publication status: Published in Chapman & Hall/CRC Monographs on Statistics and Applied Probability.108(2007): pp. 1-237
Handle: RePEc:pra:mprapa:39563

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Keywords: Estimation in time series; linear time series; model specification; nonlinear time series; nonparametric method; semiparametric method;

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