ASYMPTOTIC NORMALITY FOR WEIGHTED SUMS OF LINEAR PROCESSES
研究了线性过程加权和的渐近正态性，适用于短记忆、长记忆或季节长记忆的线性过程，以及GARCH等模型，对非参数回归估计的渐近性质推导有帮助。
We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular, they are applicable to GARCH and ARCH(∞) models and to their squares. They are also useful in deriving asymptotic normality of kernel-type estimators of a nonparametric regression function with short or long memory moving average errors.