A1978
Title: Unified inference for predictive mean and quantile regressions via empirical likelihood
Authors: Seok Young Hong - Nanyang Technological University (Singapore) [presenting]
Abstract: An empirical likelihood framework is developed for testing return predictability in the conditional mean and conditional quantiles. A unified chi-square limit theory is established across a broad spectrum of predictor persistence, including stationary, mildly integrated, nearly integrated, unit-root, and mildly explosive cases. Two complementary approaches are provided to handle the unknown intercept: (i) a sample-splitting approach under relaxed regularity conditions and (ii) a new two-stage method that improves efficiency and accommodates quantile inference, where sample-splitting is infeasible. The finite-sample bias of the two-stage method is examined, and a bias-correction scheme and gradually saturated weights are proposed that improve performance under high persistence. Simulation evidence demonstrates that the tests exhibit competitive size and power across persistence classes, with notable gains in quantile predictability. An empirical application to the U.S. stock market shows modest evidence of mean predictability, whereas quantile-based inference reveals stronger and economically relevant predictability in the tails of the return distribution.
