Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator

作者：Su Liangjun; Ullah Aman^*; Wang Yun

来源：Empirical Economics, 2013, 45(2): 1009-1024.

DOI：10.1007/s00181-012-0641-x

• Covariance matrix
• Local linear estimation
• Productivity
• Relative efficiency

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.

• 出版日期2013-10
• 单位对外经济贸易大学