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Empirical Likelihood and Quantile Methods for Time Series

Efficiency, Robustness, Optimality, and Prediction

  • Yan Liu
  • Fumiya Akashi
  • Masanobu Taniguchi

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Also part of the JSS Research Series in Statistics book sub series (JSSRES)

Table of contents

  1. Front Matter
    Pages i-x
  2. Yan Liu, Fumiya Akashi, Masanobu Taniguchi
    Pages 1-27
  3. Yan Liu, Fumiya Akashi, Masanobu Taniguchi
    Pages 29-57
  4. Yan Liu, Fumiya Akashi, Masanobu Taniguchi
    Pages 59-86
  5. Yan Liu, Fumiya Akashi, Masanobu Taniguchi
    Pages 87-108
  6. Yan Liu, Fumiya Akashi, Masanobu Taniguchi
    Pages 109-130
  7. Back Matter
    Pages 131-136

About this book

Introduction

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.

Keywords

Empirical Likelihood Quantile Score Heavy Tail Efficiency Robustness

Authors and affiliations

  • Yan Liu
    • 1
  • Fumiya Akashi
    • 2
  • Masanobu Taniguchi
    • 3
  1. 1.Kyoto University/RIKEN AIPKyotoJapan
  2. 2.Waseda UniversityTokyoJapan
  3. 3.Waseda UniversityTokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-0152-9
  • Copyright Information The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-981-10-0151-2
  • Online ISBN 978-981-10-0152-9
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site
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