The Proximal Bootstrap for Finite-Dimensional Regularized Estimators

Jessie Li

AEA Papers and Proceedings, 2021, vol. 111, 616-20

Abstract: We propose a proximal bootstrap that can consistently estimate the limiting distribution of √n consistent estimators with nonstandard asymptotic distributions in a computationally efficient manner by formulating the proximal bootstrap estimator as the solution to a convex optimization problem, which can have a closed-form solution for certain designs. This paper considers the application to finite-dimensional regularized estimators, such as the Lasso, ℓ1-norm regularized quantile regression, ℓ1-norm support vector regression, and trace regression via nuclear norm regularization.

JEL-codes: C15 C51 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.aeaweb.org/doi/10.1257/pandp.20211036 (application/pdf)
https://www.aeaweb.org/doi/10.1257/pandp.20211036.appx (application/pdf)
https://www.aeaweb.org/doi/10.1257/pandp.20211036.ds (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aea:apandp:v:111:y:2021:p:616-20

Ordering information: This journal article can be ordered from
https://www.aeaweb.org/subscribe.html

DOI: 10.1257/pandp.20211036

Access Statistics for this article

AEA Papers and Proceedings is currently edited by William Johnson and Kelly Markel

More articles in AEA Papers and Proceedings from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().