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The Review of Economics and Statistics Vol. 107 No. 6 2025

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

Abstract

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
Issue
6
Pages
1714-1723
Language
en
Sources
openalex crossref

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