Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.
Higher-Order Neyman Orthogonality in Moment-Condition Models
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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.
fields
econ.EM 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Higher-Order Debiased Estimators for General Treatment Models
Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.