论文标题
基于经验可能性的Portmanteau测试，用于自回归移动平均模型，并具有无限差异创新
Empirical likelihood-based portmanteau tests for autoregressive moving average models with possible infinite variance innovation
论文作者
论文摘要
在文献中，检查拟合的自回归移动平均值（ARMA）模型是否足够，而当前使用的测试可能会遇到尺寸失真问题时，当基础自动回归型号的持久性较低时，这是一项重要的任务。为了填补这一空白，本文提出了两个基于经验可能性的Portmanteau测试。第一个是天真的，但可以用作基准，第二个是针对无限差异创新的案例。零假设下的渐近分布是在轻度力矩条件下得出的，并且通过模拟实验和两个真实的数据示例证明了它们的实用性。
It is an important task in the literature to check whether a fitted autoregressive moving average (ARMA) model is adequate, while the currently used tests may suffer from the size distortion problem when the underlying autoregressive models have low persistence. To fill this gap, this paper proposes two empirical likelihood-based portmanteau tests. The first one is naive but can serve as a benchmark, and the second is for the case with infinite variance innovations. The asymptotic distributions under the null hypothesis are derived under mild moment conditions, and their usefulness is demonstrated by simulation experiments and two real data examples.
