Factor-Augmented Machine Learning Panel Regressions
Andrii Babii, Luca Barbaglia, Eric Ghysels, Jonas Striaukas
econ.EM math.ST stat.ME stat.ML
Abstract
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.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.