Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2006
Abstract
We define a three-step procedure for more efficient estimation of the nonparametric regression mean with nonparametric autocorrelated errors. The procedure is based upon a nonparametric prewhitening transformation of the dependent variable that has to be estimated from the data by a local polynomial technique. We establish the asymptotic distribution of our estimator under weak dependence conditions and show that it is more efficient than the conventional local polynomial estimator. Furthermore, we consider criterion functions based on the linear exponential family, which include the local polynomial least squares criterion as a special case. Simulation evidence suggests that significant gains can be achieved in finite samples with our approach.The authors thank Oliver Linton for his many constructive and helpful suggestions. The very insightful comments from the referees are also gratefully acknowledged. The second author gratefully acknowledges financial support from the Academic Senate, UCR.
Discipline
Econometrics | Economic Theory
Research Areas
Econometrics
Publication
Econometric Theory
Volume
22
Issue
1
First Page
98
Last Page
126
ISSN
0266-4666
Identifier
10.1017/S026646660606004X
Publisher
Cambridge University Press
Citation
SU, Liangjun and ULLAH, Aman.
More efficient estimation in nonparametric regression with nonparametric autocorrelated errors. (2006). Econometric Theory. 22, (1), 98-126.
Available at: https://ink.library.smu.edu.sg/soe_research/1998
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/S026646660606004X