Robust covariance estimation for quantile regression

Quantile regression is increasingly used by practitioners, but there are still some misconceptions about how difficult it is to obtain valid standard errors in this context. In this presentation I discuss the estimation of the covariance matrix of the quantile regression estimator, focusing special attention on the case where the regression errors may be heteroskedastic and/or “clustered”. Specification tests to detect heteroskedasticity and intra-cluster correlation are discussed, and small simulation studies illustrate the finite sample performance of the tests and of the covariance matrix estimators. The presentation concludes with a brief description of qreg2, which is a wrapper for qreg that implements all the methods discussed in the presentation.