Skip to main content
SpringerLink
Log in
Book cover

Nonlinear Time Series

Nonparametric and Parametric Methods

  • Book
  • © 2003

Overview

Authors:
  1. Jianqing Fan

    1. Department of Operations Research and Financial Engineering, Princeton University, Princeton, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Qiwei Yao

    1. Department of Statistics, London School of Economics, London, UK

    View author publications

    You can also search for this author in PubMed   Google Scholar

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (10 chapters)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. Introduction

    Pages 1-27

  3. Characteristics of Time Series

    Pages 29-88

  4. ARMA Modeling and Forecasting

    Pages 89-123

  5. Parametric Nonlinear Time Series Models

    Pages 125-192

  6. Nonparametric Density Estimation

    Pages 193-214

  7. Smoothing in Time Series

    Pages 215-274

  8. Spectral Density Estimation and Its Applications

    Pages 275-312

  9. Nonparametric Models

    Pages 313-403

  10. Model Validation

    Pages 405-439

  11. Nonlinear Prediction

    Pages 441-486

  12. Back Matter

    Pages 487-551
    Download chapter PDF
Back to top

Keywords

About this book

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.

Reviews

From the reviews:

“The book will particularly appeal to those in the economic sciences and financial engineering who have a solid background in linear time series models and methods. … I would recommend it to postgraduate students who are interested in learning about recent developments in non-linear and non-parametric time series modelling as well as in understanding the use of complex parametric non-linear and non-parametric time series models in practice.” (Jiti Gao, Australian Journal of Agricultural and Resource Economics, Vol. 49, 2005)

Authors and Affiliations

  • Department of Operations Research and Financial Engineering, Princeton University, Princeton, USA

    Jianqing Fan

  • Department of Statistics, London School of Economics, London, UK

    Qiwei Yao

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation