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Factor-Augmented Machine Learning Panel Regressions

Author

Listed:
  • Andrii Babii
  • Luca Barbaglia
  • Eric Ghysels
  • Jonas Striaukas

Abstract

This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks. We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors. The estimator can take advantage of the mixed-frequency group structure in the time-series dimension. Theory shows that it can outperform the standard LASSO estimator both for prediction and estimation while allowing for cross-sectional dependence.

Suggested Citation

  • Andrii Babii & Luca Barbaglia & Eric Ghysels & Jonas Striaukas, 2026. "Factor-Augmented Machine Learning Panel Regressions," Papers 2607.06368, arXiv.org.
    • Handle: RePEc:arx:papers:2607.06368
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    References listed on IDEAS

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    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. Jad Beyhum & Eric Gautier, 2022. "Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 270-281, December.
    3. Maximilian Rücker & Michael Vogt & Oliver Linton & Christopher Walsh, 2025. "Estimation and inference in high‐dimensional panel data models with interactive fixed effects," Quantitative Economics, Econometric Society, vol. 16(4), pages 1457-1509, November.
    4. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    6. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    7. Freeman, Hugo & Weidner, Martin, 2023. "Linear panel regressions with two-way unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 237(1).
    8. Andrii Babii & Eric Ghysels & Junsu Pan, 2022. "Tensor PCA for Factor Models," Papers 2212.12981, arXiv.org, revised Mar 2025.
    9. Hansen, Christian & Liao, Yuan, 2019. "The Factor-Lasso And K-Step Bootstrap Approach For Inference In High-Dimensional Economic Applications," Econometric Theory, Cambridge University Press, vol. 35(3), pages 465-509, June.
    10. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    11. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
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