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On asymmetry and quantile estimation of the stochastic frontier model

Author

Listed:
  • William C. Horrace

    (Syracuse University)

  • Christopher F. Parmeter

    (University of Miami)

  • Ian A. Wright

    (University of Miami)

Abstract

Quantile regression has become common in applied economic research. Recently, these methods have been adapted for use with the stochastic frontier model. However, the composed nature of the error term is ignored, drawing into question if a “stochastic” quantile frontier is actually estimated. Here we demonstrate that a particular distributional pair is consistent with the intent of these earlier proposals but is not in fact a quantile estimator. An interesting feature of this distributional pairing is that both distributions can be asymmetric. We further discuss the identification and practical issues associated with this framework.

Suggested Citation

  • William C. Horrace & Christopher F. Parmeter & Ian A. Wright, 2024. "On asymmetry and quantile estimation of the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 61(1), pages 19-36, February.
    • Handle: RePEc:kap:jproda:v:61:y:2024:i:1:d:10.1007_s11123-023-00673-4
    DOI: 10.1007/s11123-023-00673-4
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    References listed on IDEAS

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    2. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
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    6. Papadopoulos, Alecos & Parmeter, Christopher F., 2021. "Type II failure and specification testing in the Stochastic Frontier Model," European Journal of Operational Research, Elsevier, vol. 293(3), pages 990-1001.
    7. Alecos Papadopoulos, 2021. "Stochastic frontier models using the Generalized Exponential distribution," Journal of Productivity Analysis, Springer, vol. 55(1), pages 15-29, February.
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