EconBase
← All papers

Penalized GMM Framework for Inference on Functionals of Nonparametric Instrumental Variable Estimators

Edvard Bakhitov

arXiv 31 Mar 2026 · Econometrics

arXiv:2603.29889 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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.

Citation extraction

73
references
188
in-text mentions
73
distinct cited
2
self-citations
15,249
main-text words

appendix boundary found by appendix_command · 42% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Dikkala, Nishanth and Lewis, Greg and Mackey, Lester and Syrgkanis,… (2020) Minimax estimation of conditional moment models1.00053100%
2Newey, Whitney K (1994) The asymptotic variance of semiparametric estimators1.00053100%
3Ichimura, Hidehiko and Newey, Whitney K (2022) The Influence Function of Semiparametric Estimators0.9568488%
4Jiafeng Chen and Xiaohong Chen and Elie Tamer (2023) Efficient Estimation of Average Derivatives in NPIV Models: Simulation Comparisons of Neural Network Estimators0.9507486%
5David Gold and Johannes Lederer and Jing Tao (2020) Inference for High-Dimensional Instrumental Variables Regression0.9285580%
6Berry, Steven T and Haile, Philip A (2014) Identification in differentiated products markets using market level data0.9209378%
7Gandhi, Amit and Houde, Jean-Fran cois (2019) Measuring substitution patterns in differentiated products industries0.88513469%
8Newey, Whitney K and Powell, James L (2003) Instrumental variable estimation of nonparametric models0.84333100%
9Singh, Rahul and Sahani, Maneesh and Gretton, Arthur (2019) Kernel instrumental variable regression0.84333100%
10Chen, Xiaohong and Pouzo, Demian (2012) Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals0.81142100%

Showing the top 10 of 73 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Debiased Machine Learning: Identification, Estimation, and Shape Constraints0.40511