A Bootstrap Causality Test for Covariance Stationary Processes

Javier Hidalgo

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: 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 non-distribution free multivariate Gaussian process, say vec (B(µ)) 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 vec (B(g(µ))) is a vector with independent Brownian motion components, it implies that inferences based on vec (B(µ)) will be difficult to implement. To circumvent this problem, we propose bootstrapping the test by two alternative, although similar, algorithms showing their validity and consistency.

Keywords: Causality tests; long range; bootstrap tests. (search for similar items in EconPapers)
Date: 2003-11
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:462

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