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

TL;DR

This paper develops structured machine learning regressions with sparse-group LASSO regularization tailored for heavy-tailed, mixed-frequency panel data, providing theoretical guarantees and improved estimation in financial and economic contexts.

Contribution

It introduces a novel theoretical framework and concentration inequalities for heavy-tailed panel data, enhancing estimation accuracy with structured regularization.

Findings

Oracle inequalities for sparse-group LASSO estimators

Effective handling of heavy-tailed, mixed-frequency data

Improved estimation in financial and economic applications

Abstract

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 $\tau$-mixing processes.