Factor-Augmented Machine Learning Panel Regressions

• URL: http://arxiv.org/abs/2607.06368v1
• Date: Tue, 07 Jul 2026 15:06:00 GMT
• Title: Factor-Augmented Machine Learning Panel Regressions
• Abstract summary: This paper develops the theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks.<n>We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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