arXiv 31 Mar 2026 · Econometrics
arXiv:2603.29889 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM estimator and provide conditions for root-n consistency and asymptotic normality of debiased functional estimates, covering both linear and nonlinear functionals. Monte Carlo experiments on average derivative show that the PGMM-based debiased estimator performs on par with the analytical debiased estimator that uses the known closed-form Riesz representer, achieving 90-96% coverage while the plug-in estimator falls below 5%. We apply our procedure to estimate mean own-price elasticities in a semiparametric demand model for differentiated products. Simulations confirm near-nominal coverage while the plug-in severely undercovers. Applied to IRI scanner data on carbonated beverages, debiased semiparametric estimates are approximately 20% more elastic compared to the logit benchmark, and debiasing corrections are heterogeneous across products, ranging from negligible to several times the standard error.
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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 | Dikkala, Nishanth and Lewis, Greg and Mackey, Lester and Syrgkanis,… (2020) Minimax estimation of conditional moment models | 1.000 | 5 | 3 | 100% |
| 2 | Newey, Whitney K (1994) The asymptotic variance of semiparametric estimators | 1.000 | 5 | 3 | 100% |
| 3 | Ichimura, Hidehiko and Newey, Whitney K (2022) The Influence Function of Semiparametric Estimators | 0.956 | 8 | 4 | 88% |
| 4 | Jiafeng Chen and Xiaohong Chen and Elie Tamer (2023) Efficient Estimation of Average Derivatives in NPIV Models: Simulation Comparisons of Neural Network Estimators | 0.950 | 7 | 4 | 86% |
| 5 | David Gold and Johannes Lederer and Jing Tao (2020) Inference for High-Dimensional Instrumental Variables Regression | 0.928 | 5 | 5 | 80% |
| 6 | Berry, Steven T and Haile, Philip A (2014) Identification in differentiated products markets using market level data | 0.920 | 9 | 3 | 78% |
| 7 | Gandhi, Amit and Houde, Jean-Fran cois (2019) Measuring substitution patterns in differentiated products industries | 0.885 | 13 | 4 | 69% |
| 8 | Newey, Whitney K and Powell, James L (2003) Instrumental variable estimation of nonparametric models | 0.843 | 3 | 3 | 100% |
| 9 | Singh, Rahul and Sahani, Maneesh and Gretton, Arthur (2019) Kernel instrumental variable regression | 0.843 | 3 | 3 | 100% |
| 10 | Chen, Xiaohong and Pouzo, Demian (2012) Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 73 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: Identification, Estimation, and Shape Constraints | 0.405 | 1 | 1 |