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A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data

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  • Yangseon Kim
  • Peter Schmidt

Abstract

This paper appliesa large number of models to three previously-analyzed data sets,and compares the point estimates and confidence intervals fortechnical efficiency levels. Classical procedures include multiplecomparisons with the best, based on the fixed effects estimates;a univariate version, marginal comparisons with the best; bootstrappingof the fixed effects estimates; and maximum likelihood givena distributional assumption. Bayesian procedures include a Bayesianversion of the fixed effects model, and various Bayesian modelswith informative priors for efficiencies. We find that fixedeffects models generally perform poorly; there is a large payoffto distributional assumptions for efficiencies. We do not findmuch difference between Bayesian and classical procedures, inthe sense that the classical MLE based on a distributional assumptionfor efficiencies gives results that are rather similar to a Bayesiananalysis with the corresponding prior. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.
  • Handle: RePEc:kap:jproda:v:14:y:2000:i:2:p:91-118
    DOI: 10.1023/A:1007801006988
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    References listed on IDEAS

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    Cited by:

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    2. William C. Horrace & Kurt E. Schnier, 2010. "Fixed-Effect Estimation of Highly Mobile Production Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(5), pages 1432-1445.
    3. Tecles, Patricia Langsch & Tabak, Benjamin M., 2010. "Determinants of bank efficiency: The case of Brazil," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1587-1598, December.
    4. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    5. Ho-chuan Huang, 2004. "Estimation of Technical Inefficiencies with Heterogeneous Technologies," Journal of Productivity Analysis, Springer, vol. 21(3), pages 277-296, May.
    6. Junrong Liu & Robin C. Sickles & E. G. Tsionas, 2017. "Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity," Econometrics, MDPI, Open Access Journal, vol. 5(3), pages 1-21, July.
    7. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    8. Liang Zhang & Wei Bao & Liang Sun, 2016. "Resources and Research Production in Higher Education: A Longitudinal Analysis of Chinese Universities, 2000–2010," Research in Higher Education, Springer;Association for Institutional Research, vol. 57(7), pages 869-891, November.
    9. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2014. "Estimation and efficiency measurement in stochastic production frontiers with ordinal outcomes," Journal of Productivity Analysis, Springer, vol. 42(1), pages 67-84, August.
    10. Fernandez, Carmen & Koop, Gary & Steel, Mark F.J., 2005. "Alternative efficiency measures for multiple-output production," Journal of Econometrics, Elsevier, vol. 126(2), pages 411-444, June.
    11. Cullinane, Kevin & Song, Dong-Wook, 2006. "Estimating the Relative Efficiency of European Container Ports: A Stochastic Frontier Analysis," Research in Transportation Economics, Elsevier, vol. 16(1), pages 85-115, January.
    12. Scott Atkinson & Jeffrey Dorfman, 2005. "Multiple Comparisons with the Best: Bayesian Precision Measures of Efficiency Rankings," Journal of Productivity Analysis, Springer, vol. 23(3), pages 359-382, July.
    13. Mikko Moilanen, 2010. "Matching and settlement patterns: The case of Norway," Papers in Regional Science, Wiley Blackwell, vol. 89(3), pages 607-623, August.
    14. Kamel Helali & Maha Kalai, 2015. "Technical Efficiency Determinants Of The Tunisian Manufacturing Industry: Stochastic Production Frontiers Estimates On Panel Data," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 40(2), pages 105-130, June.
    15. Lovell, Knox, 2001. "Future Research Opportunities in Efficiency and Productivity Analysis," Efficiency Series Papers 2001/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    16. Deng, Na-Qian & Liu, Li-Qiu & Deng, Ying-Zhi, 2018. "Estimating the effects of restructuring on the technical and service-quality efficiency of electricity companies in China," Utilities Policy, Elsevier, vol. 50(C), pages 91-100.
    17. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    18. Chand, Narendra & Kerr, Geoffrey N. & Bigsby, Hugh, 2015. "Production efficiency of community forest management in Nepal," Forest Policy and Economics, Elsevier, vol. 50(C), pages 172-179.

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