Non‐parametric short‐ and long‐run Granger causality testing in the frequency domain

Abstract

Herein, we propose a novel non-parametric frequency Granger causality test. We apply a filtering process in the time domain to remove possible spurious causality, thereby eliminating potential interference. Thereafter, in the frequency domain, we perform a local kernel regression for each frequency and test the non-causality hypothesis from the distance between each estimate to zero. We provide asymptotic results for strict stationary series concerning 𝛼-mixing conditions. Our method can also perform group causality tests, a feature that is absent in most alternative methods. Monte Carlo experiments illustrate that our method is comparable, and in some cases, performs better than alternative methods in the literature. Finally, we test the causality between monetary policy variables and stock prices.