Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Bootstrap-based inference for cube root asymptotics

Tools
- Tools
+ Tools

Cattaneo, Matias D., Jansson, Michael and Nagasawa, Kenichi (2020) Bootstrap-based inference for cube root asymptotics. Econometrica, 88 (5). pp. 2203-2219. doi:10.3982/ECTA17950

[img]
Preview
PDF
WRAP-bootstrap-based-inference-cube-root-asymptotics-Nagasawa-2020.pdf - Accepted Version - Requires a PDF viewer.

Download (833Kb) | Preview
Official URL: https://doi.org/10.3982/ECTA17950

Request Changes to record.

Abstract

This paper proposes a valid bootstrap‐based distributional approximation for M‐estimators exhibiting a Chernoff (1964)‐type limiting distribution. For estimators of this kind, the standard nonparametric bootstrap is inconsistent. The method proposed herein is based on the nonparametric bootstrap, but restores consistency by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy‐to‐implement resampling method for inference that is conceptually distinct from other available distributional approximations. We illustrate the applicability of our results with four examples in econometrics and machine learning.

Item Type: Journal Article
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
Divisions: Faculty of Social Sciences > Economics
Library of Congress Subject Headings (LCSH): Decision making -- Mathematical models, Bootstrap (Statistics), Estimation theory
Journal or Publication Title: Econometrica
Publisher: Blackwell Publishing
ISSN: 0012-9682
Official Date: 25 September 2020
Dates:
DateEvent
25 September 2020Published
1 June 2020Accepted
Date of first compliant deposit: 9 June 2020
Volume: 88
Number: 5
Page Range: pp. 2203-2219
DOI: 10.3982/ECTA17950
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: The copyright to this article is held by the Econometric Society, http://www.econometricsociety.org/. It may be downloaded, printed and reproduced only for personal or classroom use. Absolutely no downloading or copying may be done for, or on behalf of, any for-profit commercial firm or for other commercial purpose without the explicit permission of the Econometric Society. All other permission requests or questions (including commercial purposes or on behalf of any for-profit entity) should be addressed to: Wiley Permissions www.wiley.com/go/rightslicensing Then select Copyright & Permissions For requests for any content not containing a ‘request permission’ link please contact permissions@wiley.com .
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
SES-1459931National Science Foundationhttp://dx.doi.org/10.13039/501100008982
SES-1947805National Science Foundationhttp://dx.doi.org/10.13039/501100008982
SES-1459967National Science Foundationhttp://dx.doi.org/10.13039/501100008982
SES-1947662National Science Foundationhttp://dx.doi.org/10.13039/501100008982
DNRF78Danmarks Grundforskningsfondhttp://dx.doi.org/10.13039/501100001732
Related URLs:
  • Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us