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| Hardcover: 237 pages Publisher: Chapman & Hall/CRC; 1 edition (March 22, 2007) Language: English Book Description Usefulin the theoretical and empirical analysis of nonlinear time seriesdata, semiparametric methods have received extensive attention in theeconomics and statistics communities over the past twenty years. Recentstudies show that semiparametric methods and models may be applied tosolve dimensionality reduction problems arising from using fullynonparametric models and methods. Answering the call for an up-to-dateoverview of the latest developments in the field, Nonlinear TimeSeries: Semiparametric and Nonparametric Methods focuses on varioussemiparametric methods in model estimation, specification testing, andselection of time series data. After a brief introduction, the bookexamines semiparametric estimation and specification methods and thenapplies these approaches to a class of nonlinear continuous-time modelswith real-world data. It also assesses some newly proposedsemiparametric estimation procedures for time series data withlong-range dependence. Even though the book only deals withclimatological and financial data, the estimation and specificationsmethods discussed can be applied to models with real-world data in manydisciplines. This resource covers key methods in time series analysisand provides the necessary theoretical details. The latest appliedfinance and financial econometrics results and applications presentedin the book enable researchers and graduate students to keep abreast ofdevelopments in the field. 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 |
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