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Neoclassical versus frontier production models? Testing for the skewness of regression residuals

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  • Kuosmanen, Timo
  • Fosgerau, Mogens

Abstract

The empirical literature on production and cost functions is divided into two strands: 1) the neoclassical approach that concentrates on model parameters, 2) the frontier approach that decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose a theoretical justification for the skewness of the inefficiency term, arguing that this skewness is the key testable hypothesis of the frontier approach. We propose to test the regression residuals for skewness to distinguish the two competing approaches. Our test builds directly upon the asymmetry of regression residuals and does not require any prior distributional assumptions.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 24208.

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Date of creation: 2009
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Handle: RePEc:pra:mprapa:24208

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Keywords: Firms and production; Frontier estimation; Hypotheses testing; Production function; Productive efficiency analysis;

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References

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  1. Ondrich, Jan & Ruggiero, John, 2001. "Efficiency measurement in the stochastic frontier model," European Journal of Operational Research, Elsevier, Elsevier, vol. 129(2), pages 434-442, March.
  2. Hyndman, R.J. & Yao, Q., 1998. "Nonparametric Estimation and Symmetry Tests for Conditional Density Functions," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 17/98, Monash University, Department of Econometrics and Business Statistics.
  3. Alicia Pérez Alonso, 2006. "A Bootstrap Approach To Test The Conditional Symmetry In Time Series Models," Working Papers. Serie AD, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) 2006-18, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  4. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-98, October.
  5. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, Elsevier, vol. 19(2-3), pages 233-238, August.
  6. Timo Kuosmanen, 2008. "Representation theorem for convex nonparametric least squares," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 11(2), pages 308-325, 07.
  7. Poitras, Geoffrey, 2006. "More on the correct use of omnibus tests for normality," Economics Letters, Elsevier, Elsevier, vol. 90(3), pages 304-309, March.
  8. Kuosmanen, Timo & Post, Thierry & Scholtes, Stefan, 2007. "Non-parametric tests of productive efficiency with errors-in-variables," Journal of Econometrics, Elsevier, Elsevier, vol. 136(1), pages 131-162, January.
  9. Kuosmanen, Timo, 2006. "Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework," Discussion Papers, MTT Agrifood Research Finland 11864, MTT Agrifood Research Finland.
  10. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 14(4), pages 460-68, October.
  11. Hanoch, Giora & Rothschild, Michael, 1972. "Testing the Assumptions of Production Theory: A Nonparametric Approach," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 80(2), pages 256-75, March-Apr.
  12. Varian, Hal R, 1984. "The Nonparametric Approach to Production Analysis," Econometrica, Econometric Society, Econometric Society, vol. 52(3), pages 579-97, May.
  13. Varian, Hal R., 1985. "Non-parametric analysis of optimizing behavior with measurement error," Journal of Econometrics, Elsevier, Elsevier, vol. 30(1-2), pages 445-458.
  14. Godfrey, L. G. & Orme, C. D., 1991. "Testing for skewness of regression disturbances," Economics Letters, Elsevier, Elsevier, vol. 37(1), pages 31-34, September.
  15. Bai, Jushan & Ng, Serena, 2001. "A consistent test for conditional symmetry in time series models," Journal of Econometrics, Elsevier, Elsevier, vol. 103(1-2), pages 225-258, July.
  16. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, Springer, vol. 38(1), pages 11-28, August.
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Citations

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Cited by:
  1. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, Springer, vol. 38(1), pages 11-28, August.
  2. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, Elsevier, vol. 34(3), pages 723-732.
  3. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, Elsevier, vol. 34(6), pages 2189-2199.

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