Stéphane Bonhomme, Koen Jochmans, Martin Weidner
arXiv 25 Sep 2026 · Econometrics
arXiv:2609.31561 · PDF · Extracted main text
Many models, such as fixed-effect models for panel or network data, are hard to estimate because they feature nuisance parameters that are both numerous and estimated imprecisely. This, in general, causes an incidental-parameter problem in the estimator of the parameters of interest. The problem can be alleviated by working with an estimating equation whose expectation is insensitive to the value of the nuisance parameters. We discuss and contrast three notions of insensitivity, also called orthogonality, in the context of likelihood models: Neyman orthogonality, Neyman orthogonality to order q, and full orthogonality. Orthogonal moments are obtained by projecting the estimating equation on nested subspaces, which are spanned by, respectively, the scores of the nuisance parameters, the first q derivatives of the likelihood ratio with respect to the nuisance parameters, and all likelihood ratios of the model. We give explicit constructions in binary-choice, count-data, and nonlinear regression models.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Neyman (1959) Optimal asymptotic tests of composite hypotheses | 1.000 | 9 | 4 | 100% |
| 2 | Bonhomme, Jochmans, and Weidner (2024) A neyman-orthogonalization approach to the incidental parameter problem self | 1.000 | 5 | 3 | 100% |
| 3 | Bonhomme and Dano (2024) Functional Differencing in Networks | 0.843 | 3 | 3 | 100% |
| 4 | Graham (2017) An econometric model of network formation with degree heterogeneity | 0.843 | 3 | 3 | 100% |
| 5 | Bonhomme (2012) Functional differencing self | 0.811 | 4 | 2 | 100% |
| 6 | Abowd, Kramarz, and Margolis (1999) High wage workers and high wage firms | 0.737 | 3 | 2 | 100% |
| 7 | Crippa (2025) Identification, Estimation, and Inference in Two-Sided Interaction Models | 0.737 | 3 | 2 | 100% |
| 8 | Ahmadpoor and Jones (2019) Decoding team and individual impact in science and invention | 0.644 | 2 | 2 | 100% |
| 9 | Bhattacharyya (1946) On some analogues of the amount of information and their use in statistical estimation | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
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