Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253605 
Year of Publication: 
2021
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 12 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 405-442
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
We consider inference about a scalar coefficient in a linear regression model. One previously considered approach to dealing with many controls imposes sparsity, that is, it is assumed known that nearly all control coefficients are (very nearly) zero. We instead impose a bound on the quadratic mean of the controls' effect on the dependent variable, which also has an interpretation as an R2-type bound on the explanatory power of the controls. We develop a simple inference procedure that exploits this additional information in general heteroskedastic models. We study its asymptotic efficiency properties and compare it to a sparsity-based approach in a Monte Carlo study. The method is illustrated in three empirical applications.
Subjects: 
High dimensional linear regression
invariance to linear reparameterizations
L2 bound
JEL: 
C12
C21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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