Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296269 
Year of Publication: 
2022
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 13 [Issue:] 1 [Year:] 2022 [Pages:] 63-94
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
This paper is concerned with estimation of functionals of a latent weight function that satisfies possibly high-dimensional multiplicative moment conditions. Main examples are functionals of stochastic discount factors in asset pricing, missing data problems, and treatment effects. We propose to estimate the latent weight function by an information theoretic approach combined with the ℓ1-penalization technique to deal with high-dimensional moment conditions under sparsity. We study asymptotic properties of the proposed method and illustrate it by a theoretical example on treatment effect analysis and empirical example on estimation of stochastic discount factors.
Subjects: 
high-dimensional model
Information theoretic approach
stochastic discount factor
treatment effect
JEL: 
C12
C14
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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