@article{0fb8f9384d3644b9bb1751db55fccd64,
title = "Robust autoregressive modeling and its diagnostic analytics with a COVID-19 related application",
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.",
keywords = "EM algorithm, influence diagnostics, matrix differential calculus, Monte Carlo simulations, skew-t innovation, time series models",
author = "Yonghui Liu and Jing Wang and V{\'i}ctor Leiva and Alejandra Tapia and Wei Tan and Shuangzhe Liu",
note = "Funding Information: The research of Y. Liu was supported by the National Social Science Fund of China [grant No. 19BTJ036]. The research of V. Leiva was partially funded by the National Agency for Research and Development (ANID) [project grant number FONDECYT 1200525] of the Chilean government under the Ministry of Science, Technology, Knowledge, and\u00A0Innovation. We would like to thank the Editor, Professor Jie Chen, the Associate Editor, and the reviewers for their constructive comments which led an improved presentation of this article. Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.",
year = "2024",
doi = "10.1080/02664763.2023.2198178",
language = "English",
volume = "51",
pages = "1318--1343",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",
number = "7",
}