  In this paper we obtain an adjusted version of the likelihood ratio test for
errors-in-variables multivariate linear regression models. The error terms are
allowed to follow a multivariate distribution in the class of the elliptical
distributions, which has the multivariate normal distribution as a special
case. We derive a modified likelihood ratio statistic that follows a
chi-squared distribution with a high degree of accuracy. Our results generalize
those in Melo and Ferrari(Advances in Statistical Analysis, 2010, 94, 75-87) by
allowing the parameter of interest to be vector-valued in the multivariate
errors-in-variables model. We report a simulation study which shows that the
proposed test displays superior finite sample behavior relative to the standard
likelihood ratio test.
