【主题】High-dimensional Predictive Quantile Regression with Mixed Roots
【报告人】范睿 (助理教授, 伦斯勒理工大学)
【摘要/Abstract】This paper studies the benefit of using adaptive LASSO for predictive quantile regression. The commonly used predictors in predictive quantile regression typically have various degrees of persistence, and exhibit different signal strength in explaining the conditional quantiles of dependent variable. We show that the adaptive LASSO methods have the consistent variable selection and the oracle properties under the simultaneous presence of stationary, unit root and cointegrated predictors. Some encouraging simulation results are reported.