PDF(Pubmed)
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.
摘要：
时间序列中的自回归模型在各个领域都很有用。在这篇文章中,我们提出了一个偏斜自回归模型。我们使用期望最大化（EM）方法估计其参数，并基于局部扰动开发影响方法以进行验证。我们获得了四种扰动策略的正常曲率，以识别有影响的观测值，然后通过蒙特卡洛模拟评估他们的表现。提供了一个金融数据分析的示例，以研究布伦特原油期货的每日对数收益率，并调查COVID-19大流行的可能影响。
