UNIFORM CONVERGENCE FOR NONPARAMETRIC ESTIMATORS WITH NONSTATIONARY DATA
为积分和分数积分时间序列的函数类建立了精确的上下一致界，并用于推导非线性协整回归模型中Nadaraya-Watson估计量和局部线性非参数估计量的最优一致收敛速度，发现局部线性估计量在一致渐近性上优于Nadaraya-Watson估计量。
Sharp upper and lower uniform bounds are established for a general class of functionals of integrated and fractionally integrated time series. The main result is used to develop optimal uniform convergence for the Nadaraya-Watson estimator and the local linear nonparametric estimator in a nonlinear cointegrating regression model. Unlike the point-wise situation, it is shown that the performance of the local linear nonparametric estimator is superior to that of the Nadaraya-Watson estimator in uniform asymptotics.