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On Gaussian Process Priors in Conditional Moment Restriction Models

Sid Kankanala

arXiv 1 Nov 2023 · Econometrics

arXiv:2311.00662 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies quasi Bayesian estimation and uncertainty quantification for an unknown function that is identified by a nonparametric conditional moment restriction. We derive contraction rates for a class of Gaussian process priors. Furthermore, we provide conditions under which a Bernstein von Mises theorem holds for the quasi-posterior distribution. As a consequence, we show that optimally weighted quasi-Bayes credible sets have exact asymptotic frequentist coverage.

Citation extraction

44
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71
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distinct cited
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main-text words

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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
1Monard, Francois, Richard Nickl, and Gabriel P Paternain (2021) b): Statistical guarantees for Bayesian uncertainty quantification in nonlinear inverse problems with Gaussian process priors1.00053100%
2Ghosal, Subhashis and Aad Van der Vaart (2017) Fundamentals of nonparametric Bayesian Inference0.87482100%
3Chen, Xiaohong and Demian Pouzo (2012) Estimation of nonparametric conditional moment models with possibly nonsmooth generalized residuals0.81142100%
4Liao, Yuan and Wenxin Jiang (2011) Posterior consistency of nonparametric conditional moment restricted models0.73732100%
5Giné, Evarist and Richard Nickl (2021) Mathematical foundations of infinite-dimensional statistical models0.69351100%
6Castillo, Ismaël and Judith Rousseau (2015) A Bernstein–von Mises theorem for smooth functionals in semiparametric models0.64422100%
7Evans, Lawrence C (2022) Partial differential equations0.64422100%
8Gugushvili, Shota, Aad van der Vaart, and Dong Yan (2020) Bayesian linear inverse problems in regularity scales, in0.64422100%
9Knapik, BT, AW van der Vaart, and JH van Zanten (2011) Bayesian inverse problems with Gaussian priors0.64422100%
10Chen, Xiaohong and Demian Pouzo (2009) Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals0.51121100%

Showing the top 10 of 44 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
1Quasi-Bayes in Latent Variable Models0.40511