Omitted variable bias of Lasso-based inference methods: A finite sample analysis

• Tác giả: Kaspar Wuthrich, Ying Zhu
• Ngôn ngữ: eng
• Ký hiệu phân loại: 511.34 Model theory
• Thông tin xuất bản: 2019
• Bộ sưu tập: Metadata
• ID: 162723

Tóm tắt

Comment: Final author version, accepted at The Review of Economics and StatisticsWe 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 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.