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Title: Shift identification in time varying regression quantiles Authors:  Subhra Sankar Dhar - IIT Kanpur (India) [presenting]
Weichi Wu - Tsinghua University (China)
Abstract: The purpose is to discuss whether time-varying quantile regression curves are the same up to the horizontal shift or not. The errors and covariates involved in the regression model are allowed to be locally stationary. We formalise this issue in a corresponding non-parametric hypothesis testing problem and develop an integrated-squared-norm based test (SIT), as well as a simultaneous confidence band (SCB) approach. The asymptotic properties of SIT and SCB under null and local alternatives are derived. We then propose valid wild bootstrap algorithms to implement SIT and SCB. Furthermore, the usefulness of the proposed methodology is illustrated for various simulated and real data related to social science and climate science.