Machine Learning Panel Data Regression with Heavy-Tailed Dependent Data: Theory and Application

Andrii Babii*, Ryan T. Ball, Eric Ghysels, Jonas Striaukas

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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 -mixing processes.
Original languageEnglish
JournalJournal of Econometrics
Number of pages25
Publication statusPublished - 26 Jul 2022
Externally publishedYes

Bibliographical note

Epub ahead of print. Published online: 26 July 2022.


  • High-dimensional panels
  • Large n and t panels
  • Mixed frequency data
  • Sparse-group LASSO
  • Fat tails

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