Inference in High-Dimensional Regression Models without the Exact or Lp sparsity - Cha, Jooyoung; Chiang, Harold D.; Sasaki, Yuya (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.