Cluster-Robust Bootstrap Inference in Quantile Regression Models
Andreas Hagemann
Journal of the American Statistical Association, 2017, vol. 112, issue 517, 446-456
Abstract:
In this article I develop a wild bootstrap procedure for cluster-robust inference in linear quantile regression models. I show that the bootstrap leads to asymptotically valid inference on the entire quantile regression process in a setting with a large number of small, heterogeneous clusters and provides consistent estimates of the asymptotic covariance function of that process. The proposed bootstrap procedure is easy to implement and performs well even when the number of clusters is much smaller than the sample size. An application to Project STAR data is provided. Supplementary materials for this article are available online.
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)
Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2016.1148610 (text/html)
Access to full text is restricted to 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:taf:jnlasa:v:112:y:2017:i:517:p:446-456
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20
DOI: 10.1080/01621459.2016.1148610
Access Statistics for this article
Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson
More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().