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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
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Handle: RePEc:taf:jnlasa:v:112:y:2017:i:517:p:446-456