  In this paper, we develop modified versions of the likelihood ratio test for
multivariate heteroskedastic errors-in-variables regression models. The error
terms are allowed to follow a multivariate distribution in the elliptical class
of distributions, which has the normal distribution as a special case. We
derive the Skovgaard adjusted likelihood ratio statistics, which follow a
chi-squared distribution with a high degree of accuracy. We conduct a
simulation study and show that the proposed tests display superior finite
sample behavior as compared to the standard likelihood ratio test. We
illustrate the usefulness of our results in applied settings using a data set
from the WHO MONICA Projection cardiovascular disease.
