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

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

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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 -mixing processes.
OriginalsprogEngelsk
Artikelnummer105315
TidsskriftJournal of Econometrics
Vol/bind237
Udgave nummer2
Antal sider25
ISSN0304-4076
DOI
StatusUdgivet - dec. 2023
Udgivet eksterntJa

Bibliografisk note

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

Emneord

  • High-dimensional panels
  • Large N and T panels
  • Mixed-frequency data
  • Sparse-group LASSO
  • Fat tails

Citationsformater