A Bootstrap Causality Test for Covariance Stationary Processes

32 Pages Posted: 21 Jul 2008

See all articles by Javier S. Hidalgo

Javier S. Hidalgo

London School of Economics & Political Science (LSE)

Date Written: November 2003

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 Є [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.

JEL Classification: C16, C53, G12

Suggested Citation

Hidalgo, Javier S., A Bootstrap Causality Test for Covariance Stationary Processes (November 2003). LSE STICERD Research Paper No. EM462, Available at SSRN: https://ssrn.com/abstract=1162624

Javier S. Hidalgo (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

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