  We study a group lasso estimator for the multivariate linear regression model
that accounts for correlated error terms. A block coordinate descent algorithm
is used to compute this estimator. We perform a simulation study with
categorical data and multivariate time series data, typical settings with a
natural grouping among the predictor variables. Our simulation studies show the
good performance of the proposed group lasso estimator compared to alternative
estimators. We illustrate the method on a time series data set of gene
expressions.
