arXiv 9 Dec 2025 · Econometrics
arXiv:2512.08423 · PDF · Extracted main text
This paper proposes a method to automatically construct or estimate Neyman-orthogonal moments in general models defined by a finite number of conditional moment restrictions (CMRs), with possibly different conditioning variables and endogenous regressors. CMRs are allowed to depend on non-parametric components, which might be flexibly modeled using Machine Learning tools, and non-linearly on finite-dimensional parameters. The key step in this construction is the estimation of Orthogonal Instrumental Variables (OR-IVs) -- "residualized" functions of the conditioning variables, which are then combined to obtain a debiased moment. We argue that computing OR-IVs necessarily requires solving potentially complicated functional equations, which depend on unknown terms. However, by imposing an approximate sparsity condition, our method finds the solutions to those equations using a Lasso-type program and can then be implemented straightforwardly. Based on this, we introduce a GMM estimator of finite-dimensional parameters (structural parameters) in a two-step framework. We derive theoretical guarantees for our construction of OR-IVs and show $\sqrt{n}$-consistency and asymptotic normality for the estimator of the structural parameters. Our Monte Carlo experiments and an empirical application on estimating firm-level production functions highlight the importance of relying on inference methods like the one proposed.
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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 | Ackerberg, Daniel A, Kevin Caves, and Garth Frazer (2015) Identification properties of recent production function estimators | 1.000 | 20 | 5 | 100% |
| 2 | Chernozhukov, Victor, Whitney K Newey, and Rahul Singh (2022) d): Automatic debiased machine learning of causal and structural effects | 1.000 | 15 | 7 | 100% |
| 3 | Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 14 | 7 | 100% |
| 4 | Levinsohn, James and Amil Petrin (2003) Estimating production functions using inputs to control for unobservables | 1.000 | 13 | 4 | 100% |
| 5 | Olley, G. Steven and Ariel Pakes (1996) The Dynamics of Productivity in the Telecommunications Equipment Industry | 1.000 | 11 | 4 | 100% |
| 6 | Ackerberg, Daniel, Xiaohong Chen, Jinyong Hahn, and Zhipeng Liao (2014) Asymptotic efficiency of semiparametric two-step GMM | 1.000 | 10 | 6 | 100% |
| 7 | Bakhitov, Edvard (2022) Automatic Debiased Machine Learning in Presence of Endogeneity | 1.000 | 10 | 5 | 100% |
| 8 | Argañaraz, Facundo and Juan Carlos Escanciano (2025) b): Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE | 1.000 | 9 | 4 | 100% |
| 9 | Belloni, Alexandre, Daniel Chen, Victor Chernozhukov, and Christian… (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 1.000 | 9 | 3 | 100% |
| 10 | Cha, Jooyoung, Harold D. Chiang, and Yuya Sasaki (2023) Inference in High-Dimensional Regression Models without the Exact or Lp Sparsity | 1.000 | 6 | 3 | 100% |
Showing the top 10 of 97 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models | 0.405 | 1 | 1 |