Inference in High-Dimensional Regression Models without the Exact or Lp sparsity
提出了一种新的高维回归和工具变量回归推断方法，无需精确稀疏性或Lp稀疏性条件，模拟显示在稀疏性较弱时优于LASSO和随机森林，并应用于智利企业面板数据生产分析。
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.