  Seemingly unrelated linear regression models are introduced in which the
distribution of the errors is a finite mixture of Gaussian components.
Identifiability conditions are provided. The score vector and the Hessian
matrix are derived. Parameter estimation is performed using the maximum
likelihood method and an Expectation-Maximisation algorithm is developed. The
usefulness of the proposed methods and a numerical evaluation of their
properties are illustrated through the analysis of a real dataset.
