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Debiased machine learning of set-identified linear models

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  • Semenova, Vira

Abstract

This paper provides estimation and inference methods for an identified set’s boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample splitting ideas, I construct a root-N consistent, uniformly asymptotically Gaussian estimator of the boundary and propose a multiplier bootstrap procedure to conduct inference. I apply this result to the Partially Linear Model, the Partially Linear IV Model and the Average Partial Derivative with an interval-valued outcome.

Suggested Citation

  • Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    • Handle: RePEc:eee:econom:v:235:y:2023:i:2:p:1725-1746
    DOI: 10.1016/j.jeconom.2022.12.010
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    Cited by:

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    2. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.

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