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Panel Data Specifications in Nonparametric Kernel Regression: An Application to Production Functions

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Author Info

  • Tomasz Czekaj

    ()
    (Department of Food and Resource Economics, University of Copenhagen)

  • Arne Henningsen

    ()
    (Department of Food and Resource Economics, University of Copenhagen)

Abstract

We discuss nonparametric regression models for panel data. A fully nonparametric panel data specification that uses the time variable and the individual identifier as additional (categorical) explanatory variables is considered to be the most suitable. We use this estimator and conventional parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we found the estimates of the fully nonparametric panel data model to be more reliable.

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File URL: http://okonomi.foi.dk/workingpapers/WPpdf/WP2013/IFRO_WP_2013_5.pdf
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Bibliographic Info

Paper provided by University of Copenhagen, Department of Food and Resource Economics in its series IFRO Working Paper with number 2013/5.

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Length: 56 pages
Date of creation: Mar 2013
Date of revision:
Handle: RePEc:foi:wpaper:2013_5

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Web page: http://www.ifro.ku.dk/
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Related research

Keywords: nonparametric kernel regression; panel data; choice of the kernel; kernels for categorical variables; production function;

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References

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  1. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9780521522717, April.
  2. Hoderlein, Stefan & White, Halbert, 2012. "Nonparametric identification in nonseparable panel data models with generalized fixed effects," Journal of Econometrics, Elsevier, vol. 168(2), pages 300-314.
  3. Hsiao, Cheng & Li, Qi & Racine, Jeffrey S., 2007. "A consistent model specification test with mixed discrete and continuous data," Journal of Econometrics, Elsevier, vol. 140(2), pages 802-826, October.
  4. Tristen Hayfield & Jeffrey S. Racine, . "Nonparametric Econometrics: The np Package," Journal of Statistical Software, American Statistical Association, vol. 27(i05).
  5. Li, Qi & Stengos, Thanasis, 1996. "Semiparametric estimation of partially linear panel data models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 389-397.
  6. Joseph G. Altonji & Rosa L. Matzkin, 2005. "Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 73(4), pages 1053-1102, 07.
  7. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
  8. Jeffrey Racine, 2008. "Nonparametric econometrics: a primer (in Russian)," Quantile, Quantile, issue 4, pages 7-56, March.
  9. Racine, Jeff, 1997. "Consistent Significance Testing for Nonparametric Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 369-78, July.
  10. Tomasz Gerard Czekaj & Arne Henningsen, 2012. "Comparing Parametric and Nonparametric Regression Methods for Panel Data: the Optimal Size of Polish Crop Farms," IFRO Working Paper 2012/12, University of Copenhagen, Department of Food and Resource Economics.
  11. Jeffery Racine & Jeffrey Hart & Qi Li, 2006. "Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 25(4), pages 523-544.
  12. Yves Croissant & Giovanni Millo, . "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, American Statistical Association, vol. 27(i02).
  13. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
  14. Kwabena Gyimah-Brempong & Jeffrey Racine, 2010. "Aid and investment in LDCs: A robust approach," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 19(2), pages 319-349.
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Cited by:
  1. Tomasz Gerard Czekaj, 2013. "Measuring the Technical Efficiency of Farms Producing Environmental Output: Parametric and Semiparametric Estimation of Multi-output Stochastic Ray Production Frontiers," IFRO Working Paper 2013/21, University of Copenhagen, Department of Food and Resource Economics.
  2. Tomasz Gerard Czekaj & Arne Henningsen, 2013. "Panel Data Nonparametric Estimation of Production Risk and Risk Preferences: An Application to Polish Dairy Farms," IFRO Working Paper 2013/6, University of Copenhagen, Department of Food and Resource Economics.

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