  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.
