Higher-Order Neyman Orthogonality in Moment-Condition Models

TL;DR

This paper introduces higher-order Neyman-orthogonal moment functions that improve robustness to nuisance estimation errors in econometric models, enabling advanced debiasing techniques.

Contribution

It develops a unified method for constructing higher-order orthogonal moments with minimal additional nuisance parameters, enhancing model robustness.

Findings

Constructed moment functions that are Neyman-orthogonal to any order.

Reduced sensitivity to nuisance estimation errors in econometric models.

Achieved higher-order debiasing with minimal additional nuisance parameters.

Abstract

We construct moment functions that are Neyman-orthogonal to a chosen order in parametric moment condition models. These moment functions reduce sensitivity to nuisance estimation error and, as such, offer a unified and tractable route to higher-order debiasing in a wide range of econometric models. The number of additional nuisance parameters required by our construction, beyond those already present in the original moment conditions, is independent of the order of orthogonalization and can be reduced to a single scalar if desired.