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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 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

Included in

Econometrics Commons

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