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Estimating Efficiency in the Presence of Extreme Outliers: A Logistic-Half Normal Stochastic Frontier Model with Application to Highway Maintenance Costs in England

In: Productivity and Inequality

Author

Listed:
  • Alexander D. Stead

    (Institute for Transport Studies, University of Leeds)

  • Phill Wheat

    (Institute for Transport Studies, University of Leeds)

  • William H. Greene

    (Stern School of Business, New York University)

Abstract
In Stochastic Frontier Analysis the presence of outliers in the data, which can often be safely ignored in other forms of linear modelling, has potentially serious consequences in that it may lead to implausibly large variation in efficiency predictions when based on the conditional mean. This motivates the development of alternative stochastic frontier specifications which are appropriate when the two-sided error has heavy tails. Several existing proposals to this effect have proceeded by specifying thick tailed distributions for both error components in order to arrive at a closed form log-likelihood. In contrast, we use simulation-based methods to pair the canonical inefficiency distributions (in this example half-normal) with a logistically distributed noise term. We apply this model to estimate cost frontiers for highways authorities in England, and compare results obtained from the conventional normal-half normal stochastic frontier model. We show that the conditional mean yields less extreme inefficiency predictions for large residuals relative to the use of the normal distribution for noise.

Suggested Citation

  • Alexander D. Stead & Phill Wheat & William H. Greene, 2018. "Estimating Efficiency in the Presence of Extreme Outliers: A Logistic-Half Normal Stochastic Frontier Model with Application to Highway Maintenance Costs in England," Springer Proceedings in Business and Economics, in: William H. Greene & Lynda Khalaf & Paul Makdissi & Robin C. Sickles & Michael Veall & Marcel-Cristia (ed.), Productivity and Inequality, pages 1-19, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-68678-3_1
    DOI: 10.1007/978-3-319-68678-3_1
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    Citations

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    Cited by:

    1. 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.
    2. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    3. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    4. Phill Wheat & Alexander D. Stead & William H. Greene, 2019. "Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England," Journal of Productivity Analysis, Springer, vol. 51(1), pages 21-38, February.
    5. Alecos Papadopoulos, 2023. "The noise error component in stochastic frontier analysis," Empirical Economics, Springer, vol. 64(6), pages 2795-2829, June.
    6. Idaira Cabrera‐Suárez & Jorge V. Pérez‐Rodríguez, 2021. "Bank branch performance and cost efficiency: A stochastic frontier panel data approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5850-5863, October.
    7. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    8. Stead, Alexander D. & Wheat, Phill & Greene, William H., 2023. "Robust maximum likelihood estimation of stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 309(1), pages 188-201.

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