A bootstrap causality test for covariance stationary processes

This paper examines a nonparametric test for Granger-causality for a vector covariance stationary linear process under, possibly, the presence of long-range dependence. We show that the test converges to a nondistribution free multivariate Gaussian process, say indexed by μ[0,1]. Because, contrary to the scalar situation, it is not possible, except in very specific cases, to find a time transformation g(μ) such that is a vector with independent Brownian motion components, it implies that inferences based on will be difficult to implement. To circumvent this problem, we propose to bootstrapping the test by two alternative, although similar, algorithms showing their validity and consistency.