Optimal Rate of Convergence for Empirical Quantiles and Distribution Functions for Time Series
针对弱依赖平稳时间序列，证明了经验分位数和经验分布函数在中心极限定理中达到最优收敛速度n^{-1/2}的Berry-Esseen结果，涵盖ARMA和GARCH等模型，对理论计量经济学研究者有参考价值。
Given a stationary sequence , we are interested in the rate of convergence in the central limit theorem of the empirical quantiles and the empirical distribution function. Under a general notion of weak dependence, we show a Berry–Esseen result with optimal rate n −1/2 . The setup includes many prominent time series models, such as functions of ARMA or (augmented) GARCH processes. In this context, optimal Berry–Esseen rates for empirical quantiles appear to be novel.