  We derive adjusted signed likelihood ratio statistics for a general class of
extreme value regression models. The adjustments reduce the error in the
standard normal approximation to the distribution of the signed likelihood
ratio statistic. We use Monte Carlo simulations to compare the finite-sample
performance of the different tests. Our simulations suggest that the signed
likelihood ratio test tends to be liberal when the sample size is not large,
and that the adjustments are effective in shrinking the size distortion. Two
real data applications are presented and discussed.
