Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application
This work addresses challenges in financial and economic data analysis where heavy tails and dependencies are common, providing a method for improved estimation in such contexts.
The paper tackles the problem of estimating machine learning regressions for heavy-tailed dependent panel data with mixed frequencies, using sparse-group LASSO regularization, and obtains oracle inequalities for pooled and fixed effects estimators with theoretical guarantees based on a new concentration inequality.
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.