Nonparametric Granger Causality Test

This paper develops a consistent nonparametric test for Granger causality in the context of strong-mixing process, which covers a large class of stationary processes including ARMA and ARCH models. The previously proposed tests require absolute regularity ($\beta$-mixing) more stringent than the strong-mixing condition. We prove the consistency of the test under a high level assumption on the approximation error of U statistic by its projection. Due to the sample splitting, the test statistic we propose is asymptotically normally distributed under the null.