Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18322
Title: Statistical inference in a panel data semiparametric regression model with serially correlated errors
Authors: You, J
Zhou, X
Keywords: Asymptotic normality
Consistency
Intraclass correlation
Panel data
Partially linear regression model
Semiparametric estimation
Serially correlated errors
Issue Date: 2006
Publisher: Elsevier Inc
Source: Journal of multivariate analysis, 2006, v. 97, no. 4, p. 844-873 How to cite?
Journal: Journal of Multivariate Analysis 
Abstract: We consider a panel data semiparametric partially linear regression model with an unknown vector β of regression coefficients, an unknown nonparametric function g(·) for nonlinear component, and unobservable serially correlated errors. The correlated errors are modeled by a vector autoregressive process which involves a constant intraclass correlation. Applying the pilot estimators of β and g(·), we construct estimators of the autoregressive coefficients, the intraclass correlation and the error variance, and investigate their asymptotic properties. Fitting the error structure results in a new semiparametric two-step estimator of β, which is shown to be asymptotically more efficient than the usual semiparametric least squares estimator in terms of asymptotic covariance matrix. Asymptotic normality of this new estimator is established, and a consistent estimator of its asymptotic covariance matrix is presented. Furthermore, a corresponding estimator of g(·) is also provided. These results can be used to make asymptotically efficient statistical inference. Some simulation studies are conducted to illustrate the finite sample performances of these proposed estimators.
URI: http://hdl.handle.net/10397/18322
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2005.04.005
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