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Nonparametric Panel Data Models, A Penalized Spline Approach

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Abstract

In this paper, we study estimation of fixed and random effects nonparametric panel data models using penalized splines and its mixed model variant. We define a "within" and a "dummy variable" estimator and show their equivalence which can be used as an argument for consistency of the dummy variable estimator when the effects are correlated with regressors. We prove nonparametric counterparts to a variety of the relations between parametric fixed and random effects estimators. Another feature of the approach followed in this paper is the potential to estimate models with heteroscedasticity and autocorrelation in the error term without difficulty. We provide a simulation experiment to illustrate the performance of the estimators.

Suggested Citation

  • Gholamreza Hajargasht, 2009. "Nonparametric Panel Data Models, A Penalized Spline Approach," CEPA Working Papers Series WP052009, School of Economics, University of Queensland, Australia.
  • Handle: RePEc:qld:uqcepa:72
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    File URL: https://economics.uq.edu.au/files/5265/WP052009.pdf
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    References listed on IDEAS

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    1. Su, Liangjun & Ullah, Aman, 2007. "More efficient estimation of nonparametric panel data models with random effects," Economics Letters, Elsevier, vol. 96(3), pages 375-380, September.
    2. Naisyin Wang, 2003. "Marginal nonparametric kernel regression accounting for within-subject correlation," Biometrika, Biometrika Trust, vol. 90(1), pages 43-52, March.
    3. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    4. Henderson, Daniel J. & Carroll, Raymond J. & Li, Qi, 2008. "Nonparametric estimation and testing of fixed effects panel data models," Journal of Econometrics, Elsevier, vol. 144(1), pages 257-275, May.
    5. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    6. 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.
    7. Henderson, Daniel J. & Ullah, Aman, 2005. "A nonparametric random effects estimator," Economics Letters, Elsevier, vol. 88(3), pages 403-407, September.
    8. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    9. Welsh A.H. & Lin X. & Carroll R.J., 2002. "Marginal Longitudinal Nonparametric Regression: Locality and Efficiency of Spline and Kernel Methods," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 482-493, June.
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    Cited by:

    1. Peter Pütz & Thomas Kneib, 2018. "A penalized spline estimator for fixed effects panel data models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 145-166, April.
    2. Peter Pütz & Thomas Kneib, 2016. "A Penalized Spline Estimator for Fixed Effects Panel Data Models," SOEPpapers on Multidisciplinary Panel Data Research 827, DIW Berlin, The German Socio-Economic Panel (SOEP).

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    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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