Robust causality test of infinite variance processes
Robust causality test of infinite variance processes
复制标题
无限方差过程的稳健因果关系检验
DOI：
10.1016/j.jeconom.2020.01.016
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发表时间：
2020
影响因子：
6.3
通讯作者：
Anna Clara Monti
中科院分区：
文献类型：
--
作者：
Fumiya Akashi;Masanobu Taniguchi;Anna Clara Monti
本章对具有无限方差创新过程的时间序列模型进行了稳健的因果检验。两个过程之间的线性依赖、反馈和因果关系的检验问题与时间序列模型本身的诊断一样重要，这在第2 - 5章中讨论。首先，我们引入向量非参数线性过程的依赖度量，并推导了Taniguchi等人（1996）在无穷方差情况下检验统计量的渐近分布。其次，我们构建了广义经验似然（GEL）检验统计量的加权版本，称为时域的自加权GEL统计量。无论模型是否具有有限方差，自加权GEL统计量的极限分布都显示为通常的卡方分布。
This chapter develops a robust causality test for time series models with infinite variance innovation processes. The testing problem of linear dependence, feedback and causality between two processes is as important as diagnostics for a time series model itself, which are discussed in Chaps. 2 – 5 . First, we introduce a measure of dependence for vector nonparametric linear processes, and derive the asymptotic distribution of the test statistic by Taniguchi et al. (1996) in the infinite variance case. Second, we construct a weighted version of the Generalized Empirical Likelihood (GEL) test statistic, called the self-weighted GEL statistic in the time domain. The limiting distribution of the self-weighted GEL statistic is shown to be the usual chi-squared one regardless of whether the model has finite variance or not.