Denis Chetverikov, Jesper R. -V. Sørensen, Aleh Tsyvinski
arXiv 20 Mar 2026 · Econometrics
arXiv:2603.20134 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we propose a triple (or double-debiased) Lasso estimator for inference on a low-dimensional parameter in high-dimensional linear regression models. The estimator is based on a moment function that satisfies not only first- but also second-order Neyman orthogonality conditions, thereby eliminating both the leading bias and the second-order bias induced by regularization. We derive an asymptotic linear representation for the proposed estimator and show that its remainder terms are never larger and are often smaller in order than those in the corresponding asymptotic linear representation for the standard double Lasso estimator. Because of this improvement, the triple Lasso estimator often yields more accurate finite-sample inference and confidence intervals with better coverage. Monte Carlo simulations confirm these gains. In addition, we provide a general recursive formula for constructing higher-order Neyman orthogonal moment functions in Z-estimation problems, which underlies the proposed estimator as a special case.
appendix boundary found by appendix_command · 65% of the source is main text. Read the extracted text to check this.
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 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 6 | 4 | 100% |
| 2 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.874 | 5 | 2 | 100% |
| 3 | Mackey, L., V. Syrgkanis, and I. Zadik (2018) Orthogonal machine learning: power and limitations, in | 0.874 | 5 | 2 | 100% |
| 4 | van de Geer, S., P. Bühlmann, Y. Ritov, and R. Dezeure (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.843 | 3 | 3 | 100% |
| 5 | Bonhomme, S., K. Johnmans, and M. Weidner (2024) A neyman-orthogonalization approach to the incidental parameter problem | 0.811 | 4 | 2 | 100% |
| 6 | Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 0.811 | 4 | 2 | 100% |
| 7 | Chernozhukov, V., C. Hansen, and M. Spindler (2015) Valid post-selection and post-regularization inference: an elementary general approach | 0.737 | 3 | 2 | 100% |
| 8 | Javanmard, A. and A. Montanari (2014) Confidence intervals and hypothesis testing for high-dimensional regression | 0.737 | 3 | 2 | 100% |
| 9 | Belloni, A. and V. Chernozhukov (2011) High Dimensional Sparse Econometric Models: An Introduction, in | 0.644 | 2 | 2 | 100% |
| 10 | van de Geer, S (2016) Estimation and Testing Under Sparsity | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 21 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | HIGHER-ORDER NEYMAN ORTHOGONALITY IN MOMENT-CONDITION MODELS | 0.405 | 1 | 1 |