  This paper establishes oracle inequalities for the prediction risk of the
empirical risk minimizer for large-dimensional linear regression. We generalize
existing results by allowing the data to be dependent and heavy-tailed. The
analysis covers both the cases of identically and heterogeneously distributed
observations. Our analysis is nonparametric in the sense that the relationship
between the regressand and the regressors is assumed to be unknown. The main
results of this paper indicate that the empirical risk minimizer achieves the
optimal performance (up to a logarithmic factor) in a dependent data setting.
