Cluster-Robust Bootstrap Inference in Quantile Regression Models
By Andreas Hagemann. 2014
78 citations recorded by the source corpus.
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.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f84d330612f4a7a5862a5131d11073df7bf81816
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In Control and Systems Engineering
In Statistics and Probability
Citations in the corpus
- Quantile regression with nonadditive fixed effects (2022, methodology): “A growing literature has developed quantile panel data estimators with additive fixed effects, including Koenker (2004), Harding and Lamarche (2009), Lamarche (2010), Canay (2011), Besstremyannaya and Golovan (2019), Galvao (2011), Ponomareva (2011), Kato et al. (2012), Rosen (2012), Galvao and…”