  This paper introduces structured machine learning regressions for
high-dimensional time series data potentially sampled at different frequencies.
The sparse-group LASSO estimator can take advantage of such time series data
structures and outperforms the unstructured LASSO. We establish oracle
inequalities for the sparse-group LASSO estimator within a framework that
allows for the mixing processes and recognizes that the financial and the
macroeconomic data may have heavier than exponential tails. An empirical
application to nowcasting US GDP growth indicates that the estimator performs
favorably compared to other alternatives and that text data can be a useful
addition to more traditional numerical data.
