MORE EFFICIENT ESTIMATION IN NONPARAMETRIC REGRESSION WITH NONPARAMETRIC AUTOCORRELATED ERRORS
提出三步法估计非参数回归均值，通过非参数预白化变换处理自相关误差，证明估计量比传统局部多项式估计更有效，模拟显示有限样本下显著改进。
We define a three-step procedure for more efficient estimation of the nonparametric regression mean with nonparametric autocorrelated errors. The procedure is based upon a nonparametric prewhitening transformation of the dependent variable that has to be estimated from the data by a local polynomial technique. We establish the asymptotic distribution of our estimator under weak dependence conditions and show that it is more efficient than the conventional local polynomial estimator. Furthermore, we consider criterion functions based on the linear exponential family, which include the local polynomial least squares criterion as a special case. Simulation evidence suggests that significant gains can be achieved in finite samples with our approach.The authors thank Oliver Linton for his many constructive and helpful suggestions. The very insightful comments from the referees are also gratefully acknowledged. The second author gratefully acknowledges financial support from the Academic Senate, UCR.