Quantile prediction with factor-augmented regression: Structural instability and model uncertainty
因子增广回归的分位数预测：结构不稳定性与模型不确定性
The quantile regression is an effective tool in modeling data with heterogeneous conditional distribution. This paper considers the time-varying coefficient quantile predictive regression with factor-augmented predictors, to capture smooth structural changes and incorporate high-dimensional data information in prediction simultaneously. Uniform consistency of the local linear quantile coefficient estimators is established under misspecification. To further improve the…