  Seemingly unrelated regression models generalize linear regression models by
considering multiple regression equations that are linked by contemporaneously
correlated disturbances. Robust inference for seemingly unrelated regression
models is considered. MM-estimators are introduced to obtain estimators that
have both a high breakdown point and a high normal efficiency. A fast and
robust bootstrap procedure is developed to obtain robust inference for these
estimators. Confidence intervals for the model parameters as well as hypothesis
tests for linear restrictions of the regression coefficients in seemingly
unrelated regression models are constructed. Moreover, in order to evaluate the
need for a seemingly unrelated regression model, a robust procedure is proposed
to test for the presence of correlation among the disturbances. The performance
of the fast and robust bootstrap inference is evaluated empirically in
simulation studies and illustrated on real data.
