A Neyman-Orthogonalization Approach to the Incidental Parameter Problem

St\'ephane Bonhomme, Koen Jochmans and Martin Weidner

Papers from arXiv.org

Abstract: A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization allows the estimator of the nuisance parameters to converge at a slower-than-parametric rate. It may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading examples are models for panel and network data that feature fixed effects. In this paper, we show how, in the conditional-likelihood setting, estimating equations can be constructed that are orthogonal to any chosen order $q$, in that their leading $q$ expected derivatives are zero. This yields estimators of target parameters that are unaffected by the presence of nuisance parameters to order $q$. In an empirical illustration, we apply our method to a fixed-effect model of team production.

Date: 2024-12, Revised 2026-02
New Economics Papers: this item is included in nep-ecm and nep-net
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http://arxiv.org/pdf/2412.10304 Latest version (application/pdf)

Related works:
Working Paper: A neyman-orthogonalization approach to the incidental parameter problem (2025)
Working Paper: A neyman-orthogonalization approach to the incidental parameter problem (2025)
Working Paper: A Neyman-Orthogonalization Approach to The Incidental Parameter Problem (2025)
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