Inference in Predictive Quantile Regressions

• Activity Number: 308
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Alex Maynard*+ and Katsumi Shimotsu and Yini Wang
• Companies: University of Guelph and Hitotsubashi University and Queen's University
• Keywords: local-to-unity ; quantile regression ; predictive testing

Abstract

This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive nonstandard distributions for the quantile regression estimator and t-statistic in terms of functionals of diffusion processes. The critical values are found to depend on both the quantile of interest and the local-to-unity parameter, which is not consistently estimable. Based on these critical values, we propose a valid Bonferroni bounds test for quantile predictability with persistent regressors. We employ this new methodology to test the ability of many commonly employed and highly persistent regressors, such as the dividend yield, earnings price ratio, book to market ratio, term spread and T-bill rate, to predict the median, shoulders, and tails of the stock return distribution.