Nonparametric regression for dependent data in the errors-in-variables problem

Toshio Honda

Global COE Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University

Abstract: We 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.

Keywords: local polynomial regression; errors-in-variables; deconvolution; ordinary smooth case; supersmooth case; linear processes; long-range dependence (search for similar items in EconPapers)
Date: 2009-11
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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