mlsanityData to 14 Oct 2026

Factor-Augmented Machine Learning Panel Regressions

arXiv 2607.06368 ↗First posted 7 Jul 2026econ.EM math.ST stat.MEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 7 Jul 2026.

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6 of 23 references.

References · 6

  1. 2019Mean estimation and regression under heavy-tailed distributions--a surveycited by 136
  2. 2021Bridging factor and sparse modelscited by 6
  3. 2019Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inferencecited by 5
  4. 2020Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Applicationcited by 5
  5. 2022Tensor PCA for Factor Modelscited by 2
  6. 2016The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applicationscited by 1

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