  We deal with a general class of extreme-value regression models introduced by
Barreto- Souza and Vasconcellos (2011). Our goal is to derive an adjusted
likelihood ratio statistic that is approximately distributed as \c{hi}2 with a
high degree of accuracy. Although the adjusted statistic requires more
computational effort than its unadjusted counterpart, it is shown that the
adjustment term has a simple compact form that can be easily implemented in
standard statistical software. Further, we compare the finite sample
performance of the three classical tests (likelihood ratio, Wald, and score),
the gradient test that has been recently proposed by Terrell (2002), and the
adjusted likelihood ratio test obtained in this paper. Our simulations favor
the latter. Applications of our results are presented. Key words: Extreme-value
regression; Gradient test; Gumbel distribution; Likelihood ratio test;
Nonlinear models; Score test; Small-sample adjustments; Wald test.
