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A Monte Carlo Analysis of Technical Inefficiency Predictors

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  • Kumbhakar, Subal C.

    (Department of Economics)

  • Löthgren, Mickael

    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

This paper studies performance of both point and interval predictors of technical inefficiency in the stochastic production frontier model using a Monte Carlo experiment. In point prediction we use the Jondrow et al. (1980) point predictor of technical inefficiency, while for interval prediction the Horrace and Schmidt (1996) and Hjalmarsson et al. (1996) results are used. When ML estimators are used we find negative bias in point predictions. MSEs are found to decline as the sample size increases. The mean empirical coverage accuracy of the confidence intervals are significantly below the theoretical confidence level for all values of the variance ratio.

Suggested Citation

  • Kumbhakar, Subal C. & Löthgren, Mickael, 1998. "A Monte Carlo Analysis of Technical Inefficiency Predictors," SSE/EFI Working Paper Series in Economics and Finance 229, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0229
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    References listed on IDEAS

    as
    1. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    2. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, University Library of Munich, Germany.
    3. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
    4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Konstantinos Giannakas & Kien Tran & Vangelis Tzouvelekas, 2003. "Predicting technical effciency in stochastic production frontier models in the presence of misspecification: a Monte-Carlo analysis," Applied Economics, Taylor & Francis Journals, vol. 35(2), pages 153-161.
    2. Leopold Simar & Paul Wilson, 2010. "Inferences from Cross-Sectional, Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 62-98.
    3. Rafaela Dios-Palomares & Jose Miguel Martínez Paz, 2004. "A spreading method to improve efficiency prediction," Economic Working Papers at Centro de Estudios Andaluces E2004/31, Centro de Estudios Andaluces.
    4. Shih-Tang Hwu & Tsu-Tan Fu & Wen-Jen Tsay, 2021. "Estimation and efficiency evaluation of stochastic frontier models with interval dependent variables," Journal of Productivity Analysis, Springer, vol. 56(1), pages 33-44, August.
    5. Phill Wheat & William Greene & Andrew Smith, 2014. "Understanding prediction intervals for firm specific inefficiency scores from parametric stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 55-65, August.

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    More about this item

    Keywords

    Bias; MSE; Point and Interval Estimators; Stochastic Production Frontier;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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