论文标题
在具有横截面依赖项的面板中测试Granger非因果关系
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
论文作者
论文摘要
本文提出了一种在面板数据上测试Granger非毒性的新方法。我们没有汇总面板成员统计信息，而是汇总其相应的p值，并表明所得的p值大约将I型误差限制为所选的显着性水平，即使面板成员的依赖。我们将我们的方法与面板数据上使用最广泛的Granger因果关系算法进行了比较，并表明我们的方法在具有横截面依赖项的大型样本量和面板的相同功率下以相同的功率产生较低的FDR。最后，我们研究了COVID-199有关在全球国家/地区衡量的已确认案件和死亡的数据，并表明我们的方法能够发现确认的案件和死亡之间的真正因果关系，而最先进的方法失败了。
This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel members are dependent. We compare our approach against the most widely used Granger causality algorithm on panel data and show that our approach yields lower FDR at the same power for large sample sizes and panels with cross-sectional dependencies. Finally, we examine COVID-19 data about confirmed cases and deaths measured in countries/regions worldwide and show that our approach is able to discover the true causal relation between confirmed cases and deaths while state-of-the-art approaches fail.
