NONPARAMETRIC TRANSFORMATION REGRESSION WITH NONSTATIONARY DATA
研究了Xiao等人提出的考虑误差相关结构的核回归估计量，发现该方法在回归变量为单位根或近单位根过程时仍能改进估计效果。
We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.