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On machine learning instrumental variable estimators

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  • Bakhitov, Edvard

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.

Suggested Citation

  • Bakhitov, Edvard, 2025. "On machine learning instrumental variable estimators," Economics Letters, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:ecolet:v:256:y:2025:i:c:s0165176525004380
    DOI: 10.1016/j.econlet.2025.112601
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    References listed on IDEAS

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    Cited by:

    1. Edvard Bakhitov, 2026. "Penalized GMM Framework for Inference on Functionals of Nonparametric Instrumental Variable Estimators," Papers 2603.29889, arXiv.org, revised Apr 2026.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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