Stéphane Bonhomme, Koen Jochmans, Whitney K. Newey, Martin Weidner
arXiv 11 May 2026 · Econometrics
arXiv:2605.10842 · PDF · DOI · OpenAlex · Extracted main text
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.
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| 1 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 4 | 3 | 100% |
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| 3 | Robins, J. M., L. Li, E. T. Tchetgen, and A. van der Vaart (2008) Higher order influence functions and minimax estimation of nonlinear functionals | 0.737 | 3 | 2 | 100% |
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| 7 | Neyman, J. and E. L. Scott (1948) Consistent estimates based on partially consistent observations | 0.644 | 2 | 2 | 100% |
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| 10 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.405 | 1 | 1 | 100% |
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