Publication Type
Journal Article
Publication Date
10-2013
Abstract
Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, 2009) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator can be further improved. First, we extend MY’s estimator to the multivariate case, and also establish the asymptotic theorem for the slope estimators; second, we propose a more efficient two-step estimator for nonparametric regression function with general parametric error covariance, and develop the corresponding asymptotic theorems. Monte Carlo study shows the relative efficiency loss of MY’s estimator in comparison with our estimator in nonparametric regression with either AR(2) errors or heteroskedastic errors. Finally, in an empirical study we apply the proposed estimator to estimate the public capital productivity to illustrate its performance in a real data setting.
Keywords
Covariance matrix, Local linear estimation, Productivity, Relative efficiency
Discipline
Econometrics
Research Areas
Econometrics
Publication
Empirical Economics
Volume
45
Issue
2
First Page
1009
Last Page
1024
ISSN
0377-7332
Identifier
10.1007/s00181-012-0641-x
Publisher
Springer Verlag
Citation
SU, Liangjun; ULLAH, Aman; and WANG, Yun.
Nonparametric Regression Estimation with General Parametric Error Covariance: A More Efficient Two-step Estimator. (2013). Empirical Economics. 45, (2), 1009-1024. Research Collection School Of Economics.
Available at: https://ink.library.smu.edu.sg/soe_research/1421
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Additional URL
http://doi.org/10.1007/s00181-012-0641-x