  This paper develops a bias correction scheme for a multivariate normal model
under a general parameterization. In the model, the mean vector and the
covariance matrix share the same parameters. It includes many important
regression models available in the literature as special cases, such as
(non)linear regression, errors-in-variables models, and so forth. Moreover,
heteroscedastic situations may also be studied within our framework. We derive
a general expression for the second-order biases of maximum likelihood
estimates of the model parameters and show that it is always possible to obtain
the second order bias by means of ordinary weighted lest-squares regressions.
We enlighten such general expression with an errors-in-variables model and also
conduct some simulations in order to verify the performance of the corrected
estimates. The simulation results show that the bias correction scheme yields
nearly unbiased estimators. We also present an empirical ilustration.
