Factor-Augmented Machine Learning Panel Regressions

Andrii Babii; Luca Barbaglia; Eric Ghysels; Jonas Striaukas

Factor-Augmented Machine Learning Panel Regressions

计量经济学 2026-07-07 v1 统计理论统计方法学机器学习

作者: Andrii Babii , Luca Barbaglia , Eric Ghysels , Jonas Striaukas

摘要

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.

引用

@article{arxiv.2607.06368,
  title  = {Factor-Augmented Machine Learning Panel Regressions},
  author = {Andrii Babii and Luca Barbaglia and Eric Ghysels and Jonas Striaukas},
  journal= {arXiv preprint arXiv:2607.06368},
  year   = {2026}
}