Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18322
DC FieldValueLanguage
dc.contributorDepartment of Applied Mathematics-
dc.creatorYou, J-
dc.creatorZhou, X-
dc.date.accessioned2015-06-23T09:15:10Z-
dc.date.available2015-06-23T09:15:10Z-
dc.identifier.issn0047-259X-
dc.identifier.urihttp://hdl.handle.net/10397/18322-
dc.language.isoenen_US
dc.publisherElsevier Incen_US
dc.subjectAsymptotic normalityen_US
dc.subjectConsistencyen_US
dc.subjectIntraclass correlationen_US
dc.subjectPanel dataen_US
dc.subjectPartially linear regression modelen_US
dc.subjectSemiparametric estimationen_US
dc.subjectSerially correlated errorsen_US
dc.titleStatistical inference in a panel data semiparametric regression model with serially correlated errorsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage844-
dc.identifier.epage873-
dc.identifier.volume97-
dc.identifier.issue4-
dc.identifier.doi10.1016/j.jmva.2005.04.005-
dcterms.abstractWe 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.-
dcterms.bibliographicCitationJournal of multivariate analysis, 2006, v. 97, no. 4, p. 844-873-
dcterms.isPartOfJournal of Multivariate Analysis-
dcterms.issued2006-
dc.identifier.isiWOS:000236339200004-
dc.identifier.scopus2-s2.0-33644701368-
dc.identifier.rosgroupidr29861-
dc.description.ros2005-2006 > Academic research: refereed > Publication in refereed journal-
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