JSS Research Series in Statistics

Empirical Likelihood and Quantile Methods for Time Series

Efficiency, Robustness, Optimality, and Prediction

Authors: Liu, Yan, Akashi, Fumiya, Taniguchi, Masanobu

  • Deals with nonstandard settings such as infinite variance rather than weakly stationary time series
  • Demonstrates that methods for parameter estimation and hypotheses testing are essentially nonparametric so that they are appropriate for economics and finance
  • Explains that the methods are advanced and unified developments of multiple-point extrapolation and interpolation in frequency domain
see more benefits

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-981-10-0152-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-981-10-0151-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makes analysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models.

About the authors

Yan Liu, Dr., Waseda University, y.liu2@kurenai.waseda.jp, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
Fumiya Akashi, Dr., Waseda University, f.akashi@kurenai.waseda.jp, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
Masanobu Taniguchi, Professor, Research Importance Position, Research Institute for Science & Engineering, Waseda University, taniguchi@waseda.jp, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan

Table of contents (5 chapters)

  • Introduction

    Liu, Yan (et al.)

    Pages 1-27

  • Parameter Estimation Based on Prediction

    Liu, Yan (et al.)

    Pages 29-57

  • Quantile Method for Time Series

    Liu, Yan (et al.)

    Pages 59-86

  • Empirical Likelihood Method for Time Series

    Liu, Yan (et al.)

    Pages 87-108

  • Self-weighted GEL Methods for Infinite Variance Processes

    Liu, Yan (et al.)

    Pages 109-130

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-981-10-0152-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-981-10-0151-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Empirical Likelihood and Quantile Methods for Time Series
Book Subtitle
Efficiency, Robustness, Optimality, and Prediction
Authors
Series Title
JSS Research Series in Statistics
Copyright
2018
Publisher
Springer Singapore
Copyright Holder
The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-10-0152-9
DOI
10.1007/978-981-10-0152-9
Softcover ISBN
978-981-10-0151-2
Series ISSN
2364-0057
Edition Number
1
Number of Pages
X, 136
Number of Illustrations
1 b/w illustrations, 9 illustrations in colour
Topics