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

Wuthrich, Kaspar ; Zhu, Ying

arXiv.org, 2021

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Título:
Omitted variable bias of Lasso-based inference methods: A finite sample analysis
Autor: Wuthrich, Kaspar ; Zhu, Ying
Assuntos: Computer simulation ; Confidence intervals ; Economic models ; Mathematics - Statistics Theory ; Monte Carlo simulation ; Parameter sensitivity ; Regression coefficients ; Statistical analysis ; Statistical inference ; Statistics - Theory
É parte de: arXiv.org, 2021
Descrição: 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 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.
Editor: Ithaca: Cornell University Library, arXiv.org
Idioma: Inglês

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