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Elements of Nonlinear Time Series Analysis and Forecasting

  • Jan G. De Gooijer

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

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Jan G. De Gooijer
    Pages 1-27
  3. Jan G. De Gooijer
    Pages 29-85
  4. Jan G. De Gooijer
    Pages 87-117
  5. Jan G. De Gooijer
    Pages 119-153
  6. Jan G. De Gooijer
    Pages 155-196
  7. Jan G. De Gooijer
    Pages 197-255
  8. Jan G. De Gooijer
    Pages 257-314
  9. Jan G. De Gooijer
    Pages 315-336
  10. Jan G. De Gooijer
    Pages 337-389
  11. Jan G. De Gooijer
    Pages 391-437
  12. Jan G. De Gooijer
    Pages 439-493
  13. Jan G. De Gooijer
    Pages 495-527
  14. Back Matter
    Pages 529-618

About this book

Introduction

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods.

The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods.

To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.

 


Keywords

nonlinear time series ARMA model AR-GARCH model time-domain linearity test model selection high dimensional tests frequency domain tests tests for serial independence nonparametric forecasting

Authors and affiliations

  • Jan G. De Gooijer
    • 1
  1. 1.University of Amsterdam AmsterdamThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-43252-6
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-43251-9
  • Online ISBN 978-3-319-43252-6
  • Series Print ISSN 0172-7397
  • Series Online ISSN 2197-568X
  • Buy this book on publisher's site