Nonparametric transformation regression with nonstationary data

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.