Statistical inference for panel data semiparametric partially linear regression models with heteroscedastic errors
We consider a panel data semiparametric partially linear regression model withÂ anÂ 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.
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Volume (Year): 101 (2010)
Issue (Month): 5 (May)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Breusch, T S & Pagan, A R, 1980.
"The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics,"
Review of Economic Studies,
Wiley Blackwell, vol. 47(1), pages 239-53, January.
- Breusch, T.S. & Pagan, A.R., . "The Lagrange multiplier test and its applications to model specification in econometrics," CORE Discussion Papers RP -412, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Stengos, T. & Li, Q., 1993.
"Adaptive Estimation in the Panel Data Error Component Model with Heteroskedasticity of Unknown Form,"
1993-4, University of Guelph, Department of Economics and Finance.
- Li, Qi & Stengos, Thanasis, 1994. "Adaptive Estimation in the Panel Data Error Component Model with Heteroskedasticity of Unknown Form," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(4), pages 981-1000, November.
- Qi Li & Aman Ullha, 1998. "Estimating partially linear panel data models with one-way error components," Econometric Reviews, Taylor & Francis Journals, vol. 17(2), pages 145-166.
- Zongwu Cai & Jianqing Fan & Qiwei Yao, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
- Gao, Jiti, 1995. "The laws of the iterated logarithm of some estimates in partly linear models," Statistics & Probability Letters, Elsevier, vol. 25(2), pages 153-162, November.
- Jianqing Fan & Qiwei Yao & Zongwu Cai, 2003. "Adaptive varying-coefficient linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 57-80.
- Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
- Shi, Jian & Lau, Tai-Shing, 2000. "Empirical Likelihood for Partially Linear Models," Journal of Multivariate Analysis, Elsevier, vol. 72(1), pages 132-148, January.
- Yu, K. & Jones, M.C., 2004. "Likelihood-Based Local Linear Estimation of the Conditional Variance Function," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 139-144, January.
- Hong, Sheng-Yan, 2002. "Normal Approximation Rate and Bias Reduction for Data-Driven Kernel Smoothing Estimator in a Semiparametric Regression Model," Journal of Multivariate Analysis, Elsevier, vol. 80(1), pages 1-20, January.
- Delecroix, Michel & Härdle, Wolfgang & Hristache, Marian, 2003. "Efficient estimation in conditional single-index regression," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 213-226, August.
- Andrews, Donald W.K., 1995. "Nonparametric Kernel Estimation for Semiparametric Models," Econometric Theory, Cambridge University Press, vol. 11(03), pages 560-586, June.
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