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Statistical inference for panel data semiparametric partially linear regression models with heteroscedastic errors

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/114649

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Title: Statistical inference for panel data semiparametric partially linear regression models with heteroscedastic errors
Related: Journal of multivariate analysis, Vol. 101, No. 5 (2010), p.1079-1101
DOI: 10.1016/j.jmva.2010.01.003
Publisher: Elsevier
Date: 2010
FoR/RFCD Code(s): 010400 Statistics
Author/Creator: You, Jinhong
Author/Creator: Zhou, Xian
Author/Creator: Zhou, Yong
Description: We consider a panel data semiparametric partially linear regression model with an unknown parameter vector for the linear parametric component, an unknown nonparametric function for the nonlinear component, and a one-way error component structure which allows unequal error variances (referred to as heteroscedasticity). We develop procedures to detect heteroscedasticity and one-way error component structure, and propose a weighted semiparametric least squares estimator (WSLSE) of the parametric component in the presence of heteroscedasticity and/or one-way error component structure. This WSLSE is asymptotically more efficient than the usual semiparametric least squares estimator considered in the literature. The asymptotic properties of the WSLSE are derived. The nonparametric component of the model is estimated by the local polynomial method. Some simulations are conducted to demonstrate the finite sample performances of the proposed testing and estimation procedures. An example of application on a set of panel data of medical expenditures in Australia is also illustrated.
Description: 23 page(s)
Subject Keyword: 010400 Statistics
Subject Keyword: asymptotic normality
Subject Keyword: heteroscedasticity
Subject Keyword: one-way error component structure
Subject Keyword: panel data
Subject Keyword: partially linear model
Subject Keyword: semiparametric estimation
Resource Type: journal article
Organisation: Macquarie University. Dept. of Actuarial Studies

Identifier: http://hdl.handle.net/1959.14/114649
Identifier: ISSN:1095-7243
Identifier: mq-rm-2010002202
Language: eng
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