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

Yonghui Liu, Jing Wang, Víctor Leiva, Alejandra Tapia, Wei Tan, Shuangzhe Liu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
4 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1-27
Number of pages27
JournalJournal of Applied Statistics
DOIs
Publication statusE-pub ahead of print - 19 Apr 2023

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