Factor-Augmented Machine Learning Panel Regressions

This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks.

We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors.

The estimator can take advantage of the mixed-frequency group structure in the time-series dimension.

Theory shows that it can outperform the standard LASSO estimator both for prediction and estimation while allowing for cross-sectional dependence.