A0803
Title: Comparing time varying regression quantiles under shift invariance
Authors: Subhra Sankar Dhar - IIT Kanpur (India)
Weichi Wu - Tsinghua University (China) [presenting]
Abstract: The aim is to investigate whether time-varying quantile regression curves are the same up to the horizontal shift or not. The errors and the covariates involved in the regression model are allowed to be locally stationary. This issue is formalized in a corresponding non-parametric hypothesis testing problem, and an integrated-squared-norm based test (SIT) and a simultaneous confidence band (SCB) approach are developed. The asymptotic properties of SIT and SCB under null and local alternatives are derived. Moreover, the asymptotic properties of these tests are also studied when the compared data sets are dependent. Then valid wild bootstrap algorithms are developed to implement SIT and SCB. Furthermore, the usefulness of the proposed methodology is illustrated by analyzing simulated and real data related to the COVID-19 outbreak.
