More efficient local polynomial estimation in nonparametric regression with autocorrelated errors Academic Article

abstract

• We propose a modification of local polynomial time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent variable that must be estimated from the data. We establish the asymptotic distribution of our estimator under weak dependence conditions. We show that the proposed estimation procedure is more efficient than the conventional local polynomial method. We also provide simulation evidence to suggest that gains can be achieved in moderate-sized samples.

authors

• Carroll, Raymond

published proceedings

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

author list (cited authors)

• Xiao, Z. J., Linton, O. B., Carroll, R. J., & Mammen, E.

citation count

• 74

complete list of authors

• Xiao, ZJ||Linton, OB||Carroll, RJ||Mammen, E

publication date

• December 2003

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION  Journal

keywords

• Kernel Regression
• Linear Process
• Prewhitening
• Time Series

Digital Object Identifier (DOI)

• 10.1198/016214503000000936

start page

• 980

end page

• 992

volume

• 98

issue

• 464

URL

• http://dx.doi.org/10.1198/016214503000000936