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An alternative corrected ordinary least squares estimator for the stochastic frontier model

Author

Listed:
  • Christopher F. Parmeter

    (University of Miami)

  • Shirong Zhao

    (Dongbei University of Finance and Economics)

Abstract

The corrected ordinary least squares (COLS) estimator of the stochastic frontier model exploits the higher order moments of the OLS residuals to estimate the parameters of the composed error. However, both “Type I” and “Type II” failures in COLS can result from finite sample bias that arises in the estimation of these higher order moments, especially in small samples. We propose a novel modification to COLS by using the first moment of the absolute value of the composite error term in place of the third moment for both the Normal-Half Normal and Normal-Exponential specifications. We demonstrate via simulations that this switch considerably reduces the occurrence of both Type I and Type II failures. These Monte Carlo simulations also reveal that our alternative COLS approach, in general, performs better than standard COLS.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:6:d:10.1007_s00181-023-02401-1
    DOI: 10.1007/s00181-023-02401-1
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    References listed on IDEAS

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    Cited by:

    1. Alecos Papadopoulos & Christopher F. Parmeter, 2024. "The wrong skewness problem in stochastic frontier analysis: a review," Journal of Productivity Analysis, Springer, vol. 61(2), pages 121-134, April.

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    More about this item

    Keywords

    Production; Efficiency; Type I failure; Type II failure; Absolute value;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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