强大的自回归建模及其诊断分析与COVID-19相关的应用程序
Yonghui Liu1, Jing Wang1, Víctor Leiva2
1School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai, People's Republic of China.
Journal of applied statistics
|June 5, 2024
概括
我们介绍了一个新的 skew-t 自动回归模型用于时间序列分析. 该模型有助于确定财务预测中的有影响力的数据点,例如COVID-19大流行对原油回报的影响.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 时间序列分析时间序列分析
背景情况:
- 自动回归模型是各种科学领域时间序列分析的基本工具.
- 现有的模型可能无法充分捕捉金融数据中经常存在的不对称性或重尾.
- 需要强大的方法来识别可能扭曲模型估计和预测的有影响力的观测.
研究的目的:
- 为增强的时间序列建模提出了一个新的skew-t自回归模型.
- 开发和验证一种方法来识别使用局部扰动分析的有影响的观测.
- 应用拟议的模型和方法来分析COVID-19大流行对布伦特原油期货的影响.
主要方法:
- 使用预期-最大化 (EM) 算法对 skew-t 自动回归模型进行参数估计.
- 基于局部干扰和计算正常曲率的影响方法的开发.
- 蒙特卡洛模拟以评估拟议的影响诊断的性能.
- 对布伦特原油期货数据的每日日志回报的应用.
主要成果:
- 斜t自回归模型为建模不对称和重尾时间序列数据提供了灵活的框架.
- 局部扰动方法有效地识别时间序列中的有影响的观测.
- 对布伦特原油期货的分析揭示了COVID-19大流行对每日日志回报的潜在影响,通过影响力点确定.
结论:
- 拟议的skew-t自回归模型和影响方法为时间序列分析提供了有价值的工具,特别是在金融领域.
- 该方法通过识别和理解异常值或有影响的数据点的影响来提高时间序列建模的稳定性.
- 该研究表明,该模型在分析真实世界的金融数据和COVID-19大流行等重大事件方面具有实际效用.
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