A Locally Robust Semiparametric Approach to Examiner IV Designs

Lonjezo Sithole

Papers from arXiv.org

Abstract: I propose a locally robust semiparametric framework for estimating causal effects using examiner IV designs when adjustment for many or continuous covariates makes saturation infeasible or produces sparse cells. The key ingredient of this approach is an orthogonal moment function that removes the first-order effect of estimation errors in the two treatment regressions defining the examiner IV. I derive the orthogonal moment function and show that it remains valid under misspecification when, for each treatment regression, either that regression or the corresponding Riesz representer in the influence function adjustment is correctly specified. The proposed framework not only allows estimation of the examiner IV using a wide range of nonparametric and machine learning techniques, including LASSO, neural networks and random forests, but also delivers root-$n$ consistent estimation and valid inference under suitable regularity conditions. I examine the finite-sample performance of the estimator through Monte Carlo simulations. I also apply the method to US patent examiners, using regularized regressions to account for differences in technology and other application characteristics.

Date: 2024-04, Revised 2026-09
New Economics Papers: this item is included in nep-big, nep-ecm and nep-inv
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2404.19144 Latest version (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:arx:papers:2404.19144

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().