In this paper we introduce a bootstrap procedure to test parameter
restrictions in vector autoregressive models which is robust in
cases of conditionally heteroskedastic error terms. The adopted
wild bootstrap method does not require any parametric
specification of the volatility process and takes contemporaneous
error correlation implicitly into account. Via a Monte Carlo
investigation empirical size and power properties of the new
method are illustrated. We compare the bootstrap approach with
standard procedures either ignoring heteroskedasticity or adopting
a heteroskedasticity consistent estimation of the relevant
covariance matrices in the spirit of the White correction. In
terms of empirical size the proposed method clearly outperforms
competing approaches without paying any price in terms of size
adjusted power. We apply the alternative tests to investigate the
potential of causal relationships linking daily prices of natural
gas and crude oil. Unlike standard inference ignoring time varying
error variances, heteroskedasticity consistent test procedures do
not deliver any evidence in favor of short run causality between
the two series