A0657
Title: Predictive quantile regression with mixed roots and increasing dimensions
Authors: Rui Fan - Rensselaer Polytechnic Institute (United States) [presenting]
Ji Hyung Lee - University of Illinois at Urbana-Champaign (United States)
Youngki Shin - McMaster University (United States)
Abstract: The benefit of using the adaptive LASSO for predictive quantile regression is studied. It is common that financial predictors in predictive quantile regression have various degrees of persistence and exhibit different signal strengths in explaining the dependent variable, such as stock returns. We show that the adaptive LASSO has the consistent variable selection and the oracle properties under the simultaneous presence of stationary, unit root and cointegrated predictors. Some encouraging simulation and out-of-sample prediction results are reported.
