On machine learning instrumental variable estimators

Edvard Bakhitov

Economics Letters, 2025, vol. 256, issue C

Abstract: This paper examines the practical challenges arising from the ill-posedness of the nonparametric instrumental variable (NPIV) estimation problem. We show that conventional NPIV series estimators struggle to estimate the underlying structural function with desired precision even in “moderate” dimensions. We argue that machine learning instrumental variable algorithms leverage sophisticated regularization techniques to mitigate these issues, achieving superior finite-sample performance.

Keywords: Nonparametric methods; Instrumental variables; Ill-posed inverse problem; Regularization; Machine learning (search for similar items in EconPapers)
JEL-codes: C14 C26 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:256:y:2025:i:c:s0165176525004380

DOI: 10.1016/j.econlet.2025.112601

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