Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241943 
Year of Publication: 
2021
Series/Report no.: 
cemmap working paper No. CWP07/21
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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