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Type II failure and specification testing in the Stochastic Frontier Model

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  • Papadopoulos, Alecos
  • Parmeter, Christopher F.

Abstract

The distributional specifications for the composite regression error term most often used in stochastic frontier analysis are inherently bounded as regards their skewness and excess kurtosis coefficients. We derive general expressions for the skewness and excess kurtosis of the composed error term in the stochastic frontier model based on the ratio of standard deviations of the two separate error components as well as theoretical ranges for the most popular empirical specifications. While these simple expressions can be used directly to assess the credibility of an assumed distributional pair, they are likely to over reject. Therefore, we develop a formal test based on the implied ratio of standard deviations for the skewness and the kurtosis. This test is shown to have impressive power compared with other tests of the specification of the composed error term. We deploy this test on a range of well-known datasets that have been used across the efficiency community. For many of them we find that the classic distribution assumptions cannot be rejected.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:293:y:2021:i:3:p:990-1001
    DOI: 10.1016/j.ejor.2020.12.065
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    Cited by:

    1. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    2. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    3. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2021. "Density deconvolution with Laplace errors and unknown variance," Journal of Productivity Analysis, Springer, vol. 56(2), pages 103-113, December.
    4. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    5. Tsionas, Mike G., 2023. "Minimax regret priors for efficiency estimation," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1279-1285.
    6. Christopher F. Parmeter & Shirong Zhao, 2023. "An alternative corrected ordinary least squares estimator for the stochastic frontier model," Empirical Economics, Springer, vol. 64(6), pages 2831-2857, June.
    7. Simos G. Meintanis & Christos K. Papadimitriou, 2022. "Goodness--of--fit tests for stochastic frontier models based on the characteristic function," Journal of Productivity Analysis, Springer, vol. 57(3), pages 285-296, June.
    8. Daniel L. Millimet & Christopher F. Parmeter, 2022. "COVID‐19 severity: A new approach to quantifying global cases and deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1178-1215, July.
    9. 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.
    10. Zhao, Shirong, 2021. "Quantile estimation of stochastic frontier models with the normal–half normal specification: A cumulative distribution function approach," Economics Letters, Elsevier, vol. 206(C).
    11. 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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