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A simulation study of DEA and parametric frontier models in the presence of heteroscedasticity

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  • Banker, Rajiv D.
  • Chang, Hsihui
  • Cooper, William W.

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  • Banker, Rajiv D. & Chang, Hsihui & Cooper, William W., 2004. "A simulation study of DEA and parametric frontier models in the presence of heteroscedasticity," European Journal of Operational Research, Elsevier, vol. 153(3), pages 624-640, March.
    • Handle: RePEc:eee:ejores:v:153:y:2004:i:3:p:624-640
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    References listed on IDEAS

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    1. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    2. Bojani, Antonio N. & Caudill, Steven B. & Ford, Jon M., 1998. "Small-sample properties of ML, COLS, and DEA estimators of frontier models in the presence of heteroscedasticity," European Journal of Operational Research, Elsevier, vol. 108(1), pages 140-148, July.
    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. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    5. Banker, Rajiv D. & Gadh, Vandana M. & Gorr, Wilpen L., 1993. "A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 67(3), pages 332-343, June.
    6. Schmidt, Peter, 1976. "On the Statistical Estimation of Parametric Frontier Production Functions," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 238-239, May.
    7. Indranil Bardhan & William Cooper & Subal Kumbhakar, 1998. "A Simulation Study of Joint Uses of Data Envelopment Analysis and Statistical Regressions for Production Function Estimation and Efficiency Evaluation," Journal of Productivity Analysis, Springer, vol. 9(3), pages 249-278, March.
    8. Richmond, J, 1974. "Estimating the Efficiency of Production," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(2), pages 515-521, June.
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    Cited by:

    1. 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.
    2. Chunping Liu & Audrey Laporte & Brian S. Ferguson, 2008. "The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1073-1087, September.
    3. Laura Di Giorgio & Abraham D Flaxman & Mark W Moses & Nancy Fullman & Michael Hanlon & Ruben O Conner & Alexandra Wollum & Christopher J L Murray, 2016. "Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    4. Cave, Joshua & Chaudhuri, Kausik & Kumbhakar, Subal C., 2023. "Dynamic firm performance and estimator choice: A comparison of dynamic panel data estimators," European Journal of Operational Research, Elsevier, vol. 307(1), pages 447-467.
    5. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    6. Banker, Rajiv D. & Zheng, Zhiqiang (Eric) & Natarajan, Ram, 2010. "DEA-based hypothesis tests for comparing two groups of decision making units," European Journal of Operational Research, Elsevier, vol. 206(1), pages 231-238, October.
    7. Yu‐Luen Ma & Nat Pope & Xiaoying Xie, 2014. "Contingent Commissions, Insurance Intermediaries, and Insurer Performance," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 17(1), pages 61-81, March.
    8. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    9. Kambiz RADMAN, 2008. "Joint of QFD & DEA & Supply Chain," Timisoara Journal of Economics, West University of Timisoara, Romania, Faculty of Economics and Business Administration, vol. 1(3), pages 271-278.
    10. K S Park & K W Lee & M S Park & D Kim, 2009. "Joint use of DEA and constrained canonical correlation analysis for efficiency valuations involving categorical variables," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1775-1785, December.
    11. Giraleas, Dimitris & Emrouznejad, Ali & Thanassoulis, Emmanuel, 2012. "Selecting between different productivity measurement approaches: An application using EU KLEMS data," MPRA Paper 37965, University Library of Munich, Germany.
    12. José Lorenzo & Isabel Sánchez, 2007. "Efficiency evaluation in municipal services: an application to the street lighting service in Spain," Journal of Productivity Analysis, Springer, vol. 27(3), pages 149-162, June.
    13. Xie, Xiaoying, 2010. "Are publicly held firms less efficient? Evidence from the US property-liability insurance industry," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1549-1563, July.
    14. Kyuseok Lee & Kyuwan Choi, 2010. "Cross redundancy and sensitivity in DEA models," Journal of Productivity Analysis, Springer, vol. 34(2), pages 151-165, October.

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