Quantile-Regression Inference With Adaptive Control of Size

Juan Carlos Escanciano (Indiana University Bloomington)
Wednesday, May 31, 2017 - 10:00am
WIAS, Erhard-Schmidt-Saal, Mohrenstraße 39, 10117 Berlin

Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This talk develops a new estimate of the asymptotic variance of regression quantiles that leads any resulting Wald-type test or confidence region to behave as well in large samples as its infeasible counterpart in which the true conditional response densities are embedded. We give explicit guidance on implementing the new variance estimator to control adaptively the size of any resulting Wald-type test. Monte Carlo evidence indicates the potential of our approach to deliver confidence intervals for quantile regression parameters with excellent coverage accuracy over different quantile levels, data-generating processes and sample sizes. We also include an empirical application. Supplementary material is available online.