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

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  • Wüthrich, Kaspar
  • Zhu, Ying

Abstract

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.

Suggested Citation

  • Wüthrich, Kaspar & Zhu, Ying, 2023. "Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis," University of California at San Diego, Economics Working Paper Series qt1gp6g9gm, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt1gp6g9gm
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    Cited by:

    1. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.

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    Keywords

    Applied Economics; Econometrics; Economics;
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