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This paper proposes a new approach for testing Granger non-causality on paneldata. Instead of aggregating panel member statistics, we aggregate theircorresponding p-values and show that the resulting p-value approximately boundsthe type I error by the chosen significance level even if the panel members aredependent. We compare our approach against the most widely used Grangercausality algorithm on panel data and show that our approach yields lower FDRat the same power for large sample sizes and panels with cross-sectionaldependencies. Finally, we examine COVID-19 data about confirmed cases anddeaths measured in countries/regions worldwide and show that our approach isable to discover the true causal relation between confirmed cases and deathswhile state-of-the-art approaches fail.

Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies