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Robust empirical likelihood for time series

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  • Kun Chen
  • Rui Huang

Abstract

This article introduces a robust frequency domain empirical likelihood inference procedure for the parametric component in the spectral densities of stationary processes. We construct the empirical likelihood function by using a new spectral estimating function to achieve robustness against contamination in the spectral density. Simulation studies demonstrate the good performance of the proposed robust frequency domain empirical likelihood method, which produces more accurate confidence regions than the ordinary empirical likelihood counterpart.

Suggested Citation

  • Kun Chen & Rui Huang, 2021. "Robust empirical likelihood for time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 4-18, January.
    • Handle: RePEc:bla:jtsera:v:42:y:2021:i:1:p:4-18
    DOI: 10.1111/jtsa.12552
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    References listed on IDEAS

    as
    1. Qin, Guoyou & Bai, Yang & Zhu, Zhongyi, 2012. "Robust empirical likelihood inference for generalized partial linear models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 32-44.
    2. Glenn, N.L. & Zhao, Yichuan, 2007. "Weighted empirical likelihood estimates and their robustness properties," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5130-5141, June.
    3. Liu, Yan, 2017. "Robust parameter estimation for stationary processes by an exotic disparity from prediction problem," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 120-130.
    4. Yan Liu & Yujie Xue & Masanobu Taniguchi, 2020. "Robust Linear Interpolation and Extrapolation of Stationary Time Series in Lp," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 229-248, March.
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