# Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application

arXiv: https://arxiv.org/abs/2008.03600 · HTML: https://mlsanity.com/paper/2008.03600/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Panel Data Nowcasting: The Case of Price-Earnings Ratios](https://mlsanity.com/paper/2307.02673/) (2023)
- [Machine Learning for Economics Research: When What and How?](https://mlsanity.com/paper/2304.00086/) (2023)
- [Econometrics of Machine Learning Methods in Economic Forecasting](https://mlsanity.com/paper/2308.10993/) (2023)
- [Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series](https://mlsanity.com/paper/2605.21542/) (2026)
- [Factor-Augmented Machine Learning Panel Regressions](https://mlsanity.com/paper/2607.06368/) (2026)

## Abstract (arXiv)

The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data can have fat tails. To that end, we leverage on a new Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed τ-mixing processes.