Nonparametric regression for dependent data in the errors-in-variables problem
AbstractWe consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression functions in this paper. We investigate how the strength of time dependence affects the asymptotic properties of the local constant and linear estimators. We treat both short-range dependent and long-range dependent linear processes in a unified way and demonstrate that the long-range dependence (LRD) of the covariates affects the asymptotic properties of the nonparametric estimators as well as the LRD of regression errors does.
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Bibliographic InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Global COE Hi-Stat Discussion Paper Series with number gd09-092.
Date of creation: Nov 2009
Date of revision:
local polynomial regression; errors-in-variables; deconvolution; ordinary smooth case; supersmooth case; linear processes; long-range dependence;
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