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.
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University of Adelaide, AustraliaReview:
"...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
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