  This paper develops a bias correction scheme for a multivariate
heteroskedastic errors-in-variables model. The applicability of this model is
justified in areas such as astrophysics, epidemiology and analytical chemistry,
where the variables are subject to measurement errors and the variances vary
with the observations. We conduct Monte Carlo simulations to investigate the
performance of the corrected estimators. The numerical results show that the
bias correction scheme yields nearly unbiased estimates. We also give an
application to a real data set.
