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Using Non-parametric Methods in Econometric Production Analysis: An Application to Polish Family Farms

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  • Czekaj, Tomasz
  • Henningsen, Arne

Abstract

Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used for estimating production functions without specifying a functional form and thus, avoiding possible misspecification errors. We use a balanced panel data set of farms specialized in crop production that is constructed from Polish FADN data for the years 2004-2007. Our analysis shows that neither the Cobb-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric estimations but many individual results are rather different.

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Bibliographic Info

Paper provided by European Association of Agricultural Economists in its series 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland with number 114280.

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Date of creation: 2011
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Handle: RePEc:ags:eaae11:114280

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Keywords: Farm Management;

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  1. Cheng Hsiao & Qi Li & Jeff Racine, 2006. "A Consistent Model Specification Test with Mixed Discrete and Continuous Data," IEPR Working Papers 06.47, Institute of Economic Policy Research (IEPR).
  2. Tristen Hayfield & Jeffrey S. Racine, . "Nonparametric Econometrics: The np Package," Journal of Statistical Software, American Statistical Association, vol. 27(i05).
  3. Racine, Jeff, 1997. "Consistent Significance Testing for Nonparametric Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 369-78, July.
  4. 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.
  5. Yves Croissant & Giovanni Millo, . "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, American Statistical Association, vol. 27(i02).
  6. 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.
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