Facundo Argañaraz, Juan Carlos Escanciano
arXiv 18 Jul 2025 · Econometrics
arXiv:2507.13788 · PDF · DOI · OpenAlex · Extracted main text
Developing robust inference for models with nonparametric Unobserved Heterogeneity (UH) is both important and challenging. We propose novel Debiased Machine Learning (DML) procedures for valid inference on functionals of UH, allowing for partial identification of multivariate target and high-dimensional nuisance parameters. Our main contribution is a full characterization of all relevant Neyman-orthogonal moments in models with nonparametric UH, where relevance means informativeness about the parameter of interest. Under additional support conditions, orthogonal moments are globally robust to the distribution of the UH. They may still involve other high-dimensional nuisance parameters, but their local robustness reduces regularization bias and enables valid DML inference. We apply these results to: (i) common parameters, average marginal effects, and variances of UH in panel data models with high-dimensional controls; (ii) moments of the common factor in the Kotlarski model with a factor loading; and (iii) smooth functionals of teacher value-added. Monte Carlo simulations show substantial efficiency gains from using efficient orthogonal moments relative to ad-hoc choices. We illustrate the practical value of our approach by showing that existing estimates of the average and variance effects of maternal smoking on child birth weight are robust.
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| Reference | Intensity | Mentions | Sections | Main text | |
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| 1 | Bonhomme, Stéphane (2012) Functional Differencing | 1.000 | 25 | 9 | 100% |
| 2 | Chamberlain, Gary (1992) Efficiency bounds for semiparametric regression | 1.000 | 14 | 6 | 100% |
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| 4 | Chernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whi… (2022) Locally robust semiparametric estimation self | 1.000 | 13 | 3 | 100% |
| 5 | Arellano, Manuel and Stéphane Bonhomme (2012) Identifying distributional characteristics in random coefficients panel data models | 1.000 | 12 | 6 | 100% |
| 6 | Honoré, Bo E and Martin Weidner (2024) Moment conditions for dynamic panel logit models with fixed effects | 1.000 | 9 | 7 | 100% |
| 7 | Chen, Xiaohong and Andres Santos (2018) Overidentification in regular models | 1.000 | 9 | 3 | 100% |
| 8 | Carrasco, Marine, Jean-Pierre Florens, and Eric Renault (2007) Chapter 77 Linear Inverse Problems in Structural Econometrics Estimation Based on Spectral Decomposition and Regularization | 1.000 | 7 | 3 | 100% |
| 9 | Luenberger, David G (1997) Optimization by vector space methods | 1.000 | 6 | 3 | 100% |
| 10 | Argañaraz, Facundo and Juan Carlos Escanciano (2023) On the Existence and Information of Orthogonal Moments For Inference self | 1.000 | 5 | 4 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R | 0.405 | 1 | 1 |
| 2 | Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications | 0.405 | 1 | 1 |