Inference in High-Dimensional Regression Models without the Exact or Lp sparsity

Jooyoung Cha; Harold D. Chiang; Yuya Sasaki

doi:10.1162/rest_a_01349

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Inference in High-Dimensional Regression Models without the Exact or Lp sparsity

Jooyoung Cha¹; Harold D. Chiang²; Yuya Sasaki¹

¹ Vanderbilt University · ² University of Wisconsin–Madison

The Review of Economics and Statistics 2025

We propose a new inference method in high-dimensional regression models and high-dimensional IV regression models. The method is shown to be valid without requiring the exact sparsity or Lp sparsity conditions. Simulation studies demonstrate superior performance of this proposed method over those based on LASSO or random forest, especially under less sparse models. We illustrate an application to production analysis with a panel of Chilean firms.

DOI: 10.1162/rest_a_01349
Volume: 107 (6)
Pages: 1714-1723
Language: en
Export: BibTeX
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