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Granger-causality in the frequency domain is an emerging tool to analyze thecausal relationship between two time series. We propose a bootstrap test onunconditional and conditional Granger-causality spectra, as well as on theirdifference, to catch particularly prominent causality cycles in relative terms.In particular, we consider a stochastic process derived applying independentlythe stationary bootstrap to the original series. Our null hypothesis is thateach causality or causality difference is equal to the median acrossfrequencies computed on that process. In this way, we are able to disambiguatecausalities which depart significantly from the median one obtained ignoringthe causality structure. Our test shows power one as the process tends tonon-stationarity, thus being more conservative than parametric alternatives. Asan example, we infer about the relationship between money stock and GDP in theEuro Area via our approach, considering inflation, unemployment and interestrates as conditioning variables. We point out that during the period 1999-2017the money stock aggregate M1 had a significant impact on economic output at allfrequencies, while the opposite relationship is significant only at highfrequencies.

A bootstrap test to detect prominent Granger-causalities across frequencies