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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: The Review of Economics and Statistics

سال: 2023

ISSN: ['0034-6535', '1530-9142']

DOI: https://doi.org/10.1162/rest_a_01128