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Heteroscedasticity in Stochastic Frontier Models: a Monte Carlo Analysis

Author

Listed:
  • Guermat, C.
  • Hadri, K.

Abstract

This paper uses Monte Carlo experimentation to investigate the finite sample properties of the maximum likelihood (ML) estimators of the half-normal stochastic frontier production functions in the presence of heteroscedasticity. It is found that when heteroscedasticity exists correcting for it leads not only to a substantial improvement of the statistical properties of estimators but also to improved efficiency and ranking measures. On the other hand correcting for heteroscedasticity when there is none has serious adverse results. Hence, there is a need for testing for heteroscedasticity and if there is any the appropriate correction should be made.

Suggested Citation

  • Guermat, C. & Hadri, K., 1999. "Heteroscedasticity in Stochastic Frontier Models: a Monte Carlo Analysis," Discussion Papers 9914, University of Exeter, Department of Economics.
    • Handle: RePEc:exe:wpaper:9914
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    Citations

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    Cited by:

    1. Tafesse, Alula & Goshu, Degye & Gelaw, Fedaku & Ademe, Alelign, 2020. "Technical Efficiency of Moringa Production: A case Study in Wolaita and Gamo Zones, Southern Ethiopia," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 9(2).
    2. Tiziana Laureti, 2008. "Modelling Exogenous Variables in Human Capital Formation through a Heteroscedastic Stochastic Frontier," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 14(1), pages 76-89, February.
    3. Tiantian Zhang & Zhenrong Liu, 2024. "Navigating Integration Through Rising Complexity: Convergence Dynamics in the ASEAN Banking Markets," Open Economies Review, Springer, vol. 35(4), pages 801-826, September.
    4. Kaddour Hadri & Julie Whittaker, 1999. "Efficiency, Environmental Contaminants and Farm Size: Testing for Links Using Stochastic Production Frontiers," Journal of Applied Economics, Universidad del CEMA, vol. 2, pages 337-356, November.
    5. repec:kap:iaecre:v:14:y:2008:i:1:p:76-89 is not listed on IDEAS
    6. Tsionas, Mike G. & Patel, Pankaj C., 2023. "Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency," International Journal of Production Economics, Elsevier, vol. 260(C).
    7. Alula Tafesse & Degye Goshu & Fekadu Gelaw & Alelign Ademe, 2021. "Technical Efficiency of Moringa Production: A case Study in Wolaita and Gamo Zones, Southern Ethiopia," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 9(2), pages 1-30, December.

    More about this item

    Keywords

    STATISTICAL ANALYSIS ; ECONOMETRICS ; ENTERPRISES;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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