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

Kaspar Wüthrich and Ying Zhu

University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

Abstract: 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.

Keywords: Applied Economics; Econometrics; Economics (search for similar items in EconPapers)
Date: 2023-07-11
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.escholarship.org/uc/item/1gp6g9gm.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:ucsdec:qt1gp6g9gm

Access Statistics for this paper

More papers in University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().