# Factor-Augmented Machine Learning Panel Regressions

arXiv: https://arxiv.org/abs/2607.06368 · HTML: https://mlsanity.com/paper/2607.06368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Mean estimation and regression under heavy-tailed distributions--a survey](https://mlsanity.com/paper/1906.04280/) (2019)
- [Bridging factor and sparse models](https://mlsanity.com/paper/2102.11341/) (2021)
- [Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference](https://mlsanity.com/paper/1909.09851/) (2019)
- [Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application](https://mlsanity.com/paper/2008.03600/) (2020)
- [Tensor PCA for Factor Models](https://mlsanity.com/paper/2212.12981/) (2022)
- [The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications](https://mlsanity.com/paper/1611.09420/) (2016)

## Abstract (arXiv)

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.