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Nonparametric estimation and testing of fixed effects panel data models

Author Info

  • Henderson, Daniel J.
  • Carroll, Raymond J.
  • Li, Qi

Abstract

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for testing the null hypothesis of random effects against fixed effects in a nonparametric panel data regression model. Simulations are used to examine the finite sample performance of the proposed estimators and the test statistics.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 144 (2008)
Issue (Month): 1 (May)
Pages: 257-275

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Handle: RePEc:eee:econom:v:144:y:2008:i:1:p:257-275
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  1. Yoon-Jae Whang & Donald W.K. Andrews, 1991. "Tests of Specification for Parametric and Semiparametric Models," Cowles Foundation Discussion Papers 968, Cowles Foundation for Research in Economics, Yale University.
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  4. Xihong Lin & Raymond J. Carroll, 2006. "Semiparametric estimation in general repeated measures problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 69-88.
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  9. Xihong Lin, 2004. "Equivalent kernels of smoothing splines in nonparametric regression for clustered/longitudinal data," Biometrika, Biometrika Trust, vol. 91(1), pages 177-193, March.
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  12. Su, Liangjun & Ullah, Aman, 2006. "Profile likelihood estimation of partially linear panel data models with fixed effects," Economics Letters, Elsevier, vol. 92(1), pages 75-81, July.
  13. Naisyin Wang, 2003. "Marginal nonparametric kernel regression accounting for within-subject correlation," Biometrika, Biometrika Trust, vol. 90(1), pages 43-52, March.
  14. Wu H. & Zhang J-T., 2002. "Local Polynomial Mixed-Effects Models for Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 883-897, September.
  15. Aman Ullah & Tae-Hwy Lee, 2000. "Nonparametric Bootstrap Tests for Neglected Nonlinearity in Time Series Regression Models," Working papers 77, Centre for Development Economics, Delhi School of Economics.
  16. Naisyin Wang & Raymond J. Carroll & Xihong Lin, 2005. "Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 147-157, March.
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