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

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  • Kaspar Wüthrich

    (University of California, San Diego, CESifo, and Ifo Institute)

  • Ying Zhu

    (University of California, San Diego)

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

  • Kaspar Wüthrich & Ying Zhu, 2023. "Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 982-997, July.
    • Handle: RePEc:tpr:restat:v:105:y:2023:i:4:p:982-997
    DOI: 10.1162/rest_a_01128
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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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