Conditional Quantile Estimation and Inference for Arch Models
针对ARCH模型提出分位数回归方法，利用条件分位数易于解释和稳健估计的优势，替代传统高斯似然方法，并简要讨论预测区间构建等推断方法。
Quantile regression methods are suggested for a class of ARCH models. Because conditional quantiles are readily interpretable in semiparametric ARCH models and are inherendy easier to estimate robustly than population moments, they offer some advantages over more familiar methods based on Gaussian likelihoods. Related inference methods, including the construction of prediction intervals, are also briefly discussed.