A Unified Inference for Predictive Quantile Regression
提出一种统一的分位数回归预测力检验方法，适用于含截距项或持续性的预测变量，通过数据分割、加权推断和随机加权自助法提升检验效果，并用S&P 500月度收益数据验证。
The asymptotic behavior of quantile regression inference becomes dramatically different when it involves a persistent predictor with zero or nonzero intercept. Distinguishing various properties of a predictor is empirically challenging. In this article, we develop a unified predictability test for quantile regression regardless of the presence of intercept and persistence of a predictor. The developed test is a novel combination of data splitting, weighted inference, and a random weighted bootstrap method. Monte Carlo simulations show that the new approach displays significantly better size and power performance than other competing methods in various scenarios, particularly when the predictive regressor contains a nonzero intercept. In an empirical application, we revisit the quantile predictability of the monthly S&P 500 returns between 1980 and 2019. Supplementary materials for this article are available online.