{"ID":6023618,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T15:00:13.465917457Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06368","arxiv_id":"2607.06368","title":"Factor-Augmented Machine Learning Panel Regressions","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.","short_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 mixe...","url_abs":"https://arxiv.org/abs/2607.06368","url_pdf":"https://arxiv.org/pdf/2607.06368v1","authors":"[\"Andrii Babii\",\"Luca Barbaglia\",\"Eric Ghysels\",\"Jonas Striaukas\"]","published":"2026-07-07T15:06:00Z","proceeding":"econ.EM","tasks":"[\"econ.EM\",\"math.ST\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}