A Neyman-Orthogonalization Approach to The Incidental Parameter Problem in Likelihood Models

Stéphane Bonhomme (), Koen Jochmans () and Martin Weidner ()
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Stéphane Bonhomme: University of Chicago
Koen Jochmans: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Martin Weidner: University of Oxford

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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 may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading examples where this is the case 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. Combining these equations with sample splitting yields higher-order bias-corrected estimators of target parameters. In an empirical application we apply our method to a fixed-effect model of team production and obtain estimates of complementarity in production and impacts of counterfactual re-allocations.

Keywords: Neyman-orthogonality; incidental parameter; higher-order bias correction; networks (search for similar items in EconPapers)
Date: 2026-09-01
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