Empirical Likelihood and Quantile Methods for Time Series
Efficiency, Robustness, Optimality, and Prediction
Authors: Liu, Yan, Akashi, Fumiya, Taniguchi, Masanobu
Free Preview- 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
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- About this book
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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
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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)
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Introduction
Pages 1-27
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Parameter Estimation Based on Prediction
Pages 29-57
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Quantile Method for Time Series
Pages 59-86
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Empirical Likelihood Method for Time Series
Pages 87-108
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Self-weighted GEL Methods for Infinite Variance Processes
Pages 109-130
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Table of contents (5 chapters)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Empirical Likelihood and Quantile Methods for Time Series
- Book Subtitle
- Efficiency, Robustness, Optimality, and Prediction
- Authors
-
- Yan Liu
- Fumiya Akashi
- Masanobu Taniguchi
- 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
