Weighted profile least squares estimation for a panel data varyingcoefficient partially linear model
This paper is concerned with inference of panel data varyingcoefficient partially linear models with a oneway error structure. The model is a natural extension of the wellknown panel data linear model (due to Baltagi 1995) to the setting of semiparametric regressions. The authors propose a weight... Full description
Journal Title:  Chinese Annals of Mathematics Series B, 2010, Vol.31(2), pp.247272 
Main Author:  Zhou, Bin 
Other Authors:  You, Jinhong , Xu, Qinfeng , Chen, Gemai 
Format:  Electronic Article 
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English 
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ID:  ISSN: 02529599 ; EISSN: 18606261 ; DOI: 10.1007/s1140100801533 
Link:  http://dx.doi.org/10.1007/s1140100801533 
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recordid:  springer_jour10.1007/s1140100801533 
title:  Weighted profile least squares estimation for a panel data varyingcoefficient partially linear model 
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ispartof:  Chinese Annals of Mathematics, Series B, 2010, Vol.31(2), pp.247272 
description:  This paper is concerned with inference of panel data varyingcoefficient partially linear models with a oneway error structure. The model is a natural extension of the wellknown panel data linear model (due to Baltagi 1995) to the setting of semiparametric regressions. The authors propose a weighted profile least squares estimator (WPLSE) and a weighted local polynomial estimator (WLPE) for the parametric and nonparametric components, respectively. It is shown that the WPLSE is asymptotically more efficient than the usual profile least squares estimator (PLSE), and that the WLPE is also asymptotically more efficient than the usual local polynomial estimator (LPE). The latter is an interesting result. According to Ruckstuhl, Welsh and Carroll (2000) and Lin and Carroll (2000), ignoring the correlation structure entirely and “pretending” that the data are really independent will result in more efficient estimators when estimating nonparametric regression with longitudinal or panel data. The result in this paper shows that this is not true when the design points of the nonparametric component have a closeness property within groups. The asymptotic properties of the proposed weighted estimators are derived. In addition, a block bootstrap test is proposed for the goodness of fit of models, which can accommodate the correlations within groups. Some simulation studies are conducted to illustrate the finite sample performances of the proposed procedures. 
language:  eng 
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identifier:  ISSN: 02529599 ; EISSN: 18606261 ; DOI: 10.1007/s1140100801533 
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