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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