Debiased machine learning of set-identified linear models

Vira Semenova

Journal of Econometrics, 2023, vol. 235, issue 2, 1725-1746

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.

Keywords: support function; interval data; debiased machine learning; partial identification (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1725-1746

DOI: 10.1016/j.jeconom.2022.12.010

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