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Robust autoregressive modeling and its diagnostic analytics with a COVID-19 related application

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Listed:
  • Yonghui Liu
  • Jing Wang
  • Víctor Leiva
  • Alejandra Tapia
  • Wei Tan
  • Shuangzhe Liu

Abstract

Autoregressive models in time series are useful in various areas. In this article, we propose a skew-t autoregressive model. We estimate its parameters using the expectation-maximization (EM) method and develop the influence methodology based on local perturbations for its validation. We obtain the normal curvatures for four perturbation strategies to identify influential observations, and then to assess their performance through Monte Carlo simulations. An example of financial data analysis is presented to study daily log-returns for Brent crude futures and investigate possible impact by the COVID-19 pandemic.

Suggested Citation

  • Yonghui Liu & Jing Wang & Víctor Leiva & Alejandra Tapia & Wei Tan & Shuangzhe Liu, 2024. "Robust autoregressive modeling and its diagnostic analytics with a COVID-19 related application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 51(7), pages 1318-1343, May.
    • Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1318-1343
    DOI: 10.1080/02664763.2023.2198178
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