Arbeitspapier
Adversarial estimation of Riesz representers
We provide an adversarial approach to estimating Riesz representers of linear functionals within arbitrary function spaces. We prove oracle inequalities based on the localized Rademacher complexity of the function space used to approximate the Riesz representer and the approximation error. These inequalities imply fast finite sample mean-squared-error rates for many function spaces of interest, such as high-dimensional sparse linear functions, neural networks and reproducing kernel Hilbert spaces. Our approach offers a new way of estimating Riesz representers with a plethora of recently introduced machine learning techniques. We show how our estimator can be used in the context of de-biasing structural/causal parameters in semi-parametric models, for automated orthogonalization of moment equations and for estimating the stochastic discount factor in the context of asset pricing.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP07/21
- Classification
-
Wirtschaft
- Event
-
Geistige Schöpfung
- (who)
-
Chernozhukov, Victor
Newey, Whitney K.
Singh, Rahul
Syrgkanis, Vasilis
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2021
- DOI
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doi:10.47004/wp.cem.2021.0720
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Chernozhukov, Victor
- Newey, Whitney K.
- Singh, Rahul
- Syrgkanis, Vasilis
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2021
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