Automatic Debiased Machine Learning of Structural Parameters with General Conditional Moments
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914189604290560 |
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| author | Argañaraz, Facundo |
| author_facet | Argañaraz, Facundo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Debiased Machine Learning of Structural Parameters with General Conditional Moments Argañaraz, Facundo Econometrics 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. |
| title | Automatic Debiased Machine Learning of Structural Parameters with General Conditional Moments |
| topic | Econometrics |
| url | https://arxiv.org/abs/2512.08423 |