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Nonlinear Time Series

Nonparametric and Parametric Methods

  • Jianqing Fan
  • Qiwei Yao

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Pages 1-27
  3. Pages 215-274
  4. Pages 313-403
  5. Pages 405-439
  6. Pages 441-486
  7. Back Matter
    Pages 487-551

About this book

Introduction

Amongmanyexcitingdevelopmentsinstatisticsoverthelasttwodecades, nonlineartimeseriesanddata-analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In spite of the fact that the - plication of nonparametric techniques in time series can be traced back to the 1940s at least, there still exists healthy and justi?ed skepticism about the capability of nonparametric methods in time series analysis. As - thusiastic explorers of the modern nonparametric toolkit, we feel obliged to assemble together in one place the newly developed relevant techniques. Theaimofthisbookistoadvocatethosemodernnonparametrictechniques that have proven useful for analyzing real time series data, and to provoke further research in both methodology and theory for nonparametric time series analysis. Modern computers and the information age bring us opportunities with challenges. Technological inventions have led to the explosion in data c- lection (e.g., daily grocery sales, stock market trading, microarray data). The Internet makes big data warehouses readily accessible. Although cl- sic parametric models, which postulate global structures for underlying systems, are still very useful, large data sets prompt the search for more re?nedstructures,whichleadstobetterunderstandingandapproximations of the real world. Beyond postulated parametric models, there are in?nite other possibilities. Nonparametric techniques provide useful exploratory tools for this venture, including the suggestion of new parametric models and the validation of existing ones.

Keywords

Time series econometrics linear optimization mathematical statistics modeling nonparametric methods statistical method statistics

Authors and affiliations

  • Jianqing Fan
    • 1
  • Qiwei Yao
    • 2
  1. 1.Department of Operations Research and Financial EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Department of StatisticsLondon School of EconomicsLondonUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-69395-8
  • Copyright Information Springer Sciences+Business Media, Inc. 2003
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-26142-3
  • Online ISBN 978-0-387-69395-8
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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