Stéphane Bonhomme, Koen Jochmans, Martin Weidner
arXiv 13 Dec 2024 · Econometrics
arXiv:2412.10304 · PDF · DOI · OpenAlex · Extracted main text
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.
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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, J (1959) Optimal asymptotic tests of composite hypotheses | 1.000 | 5 | 3 | 100% |
| 2 | Waterman, R. P. and B. G. Lindsay (1996) Projected score methods for approximating conditional scores | 1.000 | 5 | 3 | 100% |
| 3 | Ahmadpoor, M. and B. F. Jones (2019) Decoding team and individual impact in science and invention | 0.928 | 4 | 3 | 100% |
| 4 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.843 | 3 | 3 | 100% |
| 5 | Mackey, L., V. Syrgkanis, and I. Zadik (2018) Orthogonal machine learning: Power and limitations | 0.843 | 3 | 3 | 100% |
| 6 | Dhaene, G. and K. Jochmans (2015) Split-panel jackknife estimation of fixed-effect models | 0.737 | 3 | 2 | 100% |
| 7 | Dhaene, G. and K. Jochmans (2015) Profile-score adjustments for incidental-parameter problems | 0.737 | 3 | 2 | 100% |
| 8 | Hahn, J. and W. K. Newey (2004) Jackknife and analytical bias reduction for nonlinear panel models | 0.737 | 3 | 2 | 100% |
| 9 | van der Vaart, A (2014) Higher order tangent spaces and influence functions | 0.737 | 3 | 2 | 100% |
| 10 | Bonhomme, S (2021) Teams: Heterogeneity, sorting, and complementarity self | 0.644 | 2 | 2 | 100% |
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