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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

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