Jackknifing in partially linear regression models with serially correlated errors
- 作者：You ; Jinhong ; Zhou ; Xian ; Chen ; Gemai
- 关键词：primary 62G05 ; secondary 62G20 ; 62M10 ; Partially linear regression model ; Serially correlated errors ; Semiparametric least-squares estimator ; Jackknife-type estimation ; Asymptotic normality ; Consistency ; Robustness
- 刊名：Journal of Multivariate Analysis
- 出版年：2005
- 期刊代码：77_0047259x
- 类别：mt
- 出版时间：February, 2005
- 卷：92
- 期：2
- 页码：386-404
- 文件大小：301 K
摘要
In this paper jackknifing technique is examined for functions of the parametric component in a partially linear regression model with serially correlated errors. By deleting partial residuals a jackknife-type estimator is proposed. It is shown that the jackknife-type estimator and the usual semiparametric least-squares estimator (SLSE) are asymptotically equivalent. However, simulation shows that the former has smaller biases than the latter when the sample size is small or moderate. Moreover, since the errors are correlated, both the Tukey type and the delta type jackknife asymptotic variance estimators are not consistent. By introducing cross-product terms, a consistent estimator of the jackknife asymptotic variance is constructed and shown to be robust against heterogeneity of the error variances. In addition, simulation results show that confidence interval estimation based on the proposed jackknife estimator has better coverage probability than that based on the SLSE, even though the latter uses the information of the error structure, while the former does not.