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The paper introduces structured machine learning regressions for heavy-taileddependent panel data potentially sampled at different frequencies. We focus onthe sparse-group LASSO regularization. This type of regularization can takeadvantage of the mixed frequency time series panel data structures and improvethe quality of the estimates. We obtain oracle inequalities for the pooled andfixed effects sparse-group LASSO panel data estimators recognizing thatfinancial and economic data can have fat tails. To that end, we leverage on anew Fuk-Nagaev concentration inequality for panel data consisting ofheavy-tailed $\tau$-mixing processes.

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