Factor-Augmented Machine Learning Panel Regressions

Andrii Babii, Luca Barbaglia, Eric Ghysels and Jonas Striaukas

Papers from arXiv.org

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.

Date: 2026-07
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