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Inference in Stochastic Frontier Models Based on Asymmetry

Author

Listed:
  • Ahmed S

    (Department of Statistics and Operational Research and Mathematics Didactics, University of Oviedo, Spain)

  • Sonia Pérez-F

    (Department of Statistics and Operational Research and Mathematics Didactics, University of Oviedo, Spain)

  • Carlos Carleos A

    (Department of Statistics and Operational Research and Mathematics Didactics, University of Oviedo, Spain)

  • Norberto C

    (Department of Statistics and Operational Research and Mathematics Didactics, University of Oviedo, Spain)

  • Pablo Martínez C

    (The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, USA)

Abstract

Stochastic frontier analysis (SFA) is often employed to study the production functions. The structure of errors is the main difference between the standard regression analysis and the stochastic frontier models; in the SFA, an independent random term with positive value is added to the usual white noise error. Conventionally, the parameters involved in the SFA are estimated and then, the convenience of using this model is tested. The authors propose to study, previously, the residuals in order to check the capacity of assuming a stochastic frontier model and then, if applicable, to estimate the parameters. With this goal, several non-parametric hypothesis testing are explored.

Suggested Citation

  • Ahmed S & Sonia Pérez-F & Carlos Carleos A & Norberto C & Pablo Martínez C, 2018. "Inference in Stochastic Frontier Models Based on Asymmetry," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 4(4), pages 99-108, January.
    • Handle: RePEc:adp:jbboaj:v:4:y:2018:i:4:p:99-108
    DOI: 10.19080/BBOAJ.2018.04.555645
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    References listed on IDEAS

    as
    1. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    2. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March.
    3. Léopold Simar & Ingrid Keilegom & Valentin Zelenyuk, 2017. "Nonparametric least squares methods for stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 47(3), pages 189-204, June.
    4. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    5. Robert Staudte, 2014. "Inference for quantile measures of skewness," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 751-768, December.
    6. Behr, Andreas & Tente, Sebastian, 2008. "Stochastic frontier analysis by means of maximum likelihood and the method of moments," Discussion Paper Series 2: Banking and Financial Studies 2008,19, Deutsche Bundesbank.
    7. Timo Kuosmanen & Mogens Fosgerau, 2009. "Neoclassical versus Frontier Production Models? Testing for the Skewness of Regression Residuals," Scandinavian Journal of Economics, Wiley Blackwell, vol. 111(2), pages 351-367, June.
    8. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    9. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
    10. Carlos Martins-Filho & Feng Yao, 2015. "Semiparametric Stochastic Frontier Estimation via Profile Likelihood," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 413-451, April.
    11. Qu Feng & William Horrace & Guiying Laura Wu, 2013. "Wrong Skewness and Finite Sample Correction in Parametric Stochastic Frontier Models Abstract: In parametric stochastic frontier models, the composed error is specified as the sum of a two-sided noise component and a one-sided inefficiency component,," Center for Policy Research Working Papers 154, Center for Policy Research, Maxwell School, Syracuse University.
    12. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    13. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    14. Wilson, Paul W., 2008. "FEAR: A software package for frontier efficiency analysis with R," Socio-Economic Planning Sciences, Elsevier, vol. 42(4), pages 247-254, December.
    15. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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