RT Journal Article
T1 Quantile-regression inference with adaptive control of size
A1 Escanciano, Juan Carlos
A1 Goh, Sze Chuan
AB Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This article 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 powerful tests of heterogeneity of quantile treatment effects in covariates with good size performance over different quantile levels, data-generating processes, and sample sizes. We also include an empirical example. Supplementary material is available online
PB Taylor & Francis
SN 0162-1459
YR 2019
FD 2019-07-01
LK https://hdl.handle.net/10016/35068
UL https://hdl.handle.net/10016/35068
LA eng
NO This work was partially supported by the Spanish Plan Nacional de I+D+I, reference number ECO2014-55858-P.
DS e-Archivo
RD 1 sept. 2024