Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis

• 成果类型: Article
• 署名作者: Wuthrich, Kaspar; Zhu, Ying
• 署名单位: University of California System; University of California San Diego; Leibniz Association; Ifo Institut; Leibniz Association; Ifo Institut
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01128
• 发表日期: 2023-07
• 页码: 982-997
• 关键词: confidence-intervals; model-selection; consistent; tests

摘要

We study the finite sample behavior of Lasso-based inference methods such as post-double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso's not selecting relevant controls. This phenomenon can occur even when the coefficients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern high-dimensional OLS-based methods and provide practical guidance.