[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/cgr/cgsser/02-06.html
   My bibliography  Save this paper

When, where and how to perform efficiency estimation

Author

Listed:
  • Oleg Badunenko

    (CGS, University of Cologne)

  • Daniel J. Henderson

    (State University of New York-Binghamton)

  • Subal C. Kumbhakar

    (State University of New York-Binghamton)

Abstract
In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar and Wilson (2008). We assess the finite sample performance of each estimator via Monte Carlo simulations and empirical examples. We find that the reliability of efficiency scores critically hinges upon the ratio of the variation in efficiency to the variation in noise. These results should be a valuable resource to both academic researchers and practitioners.

Suggested Citation

  • Oleg Badunenko & Daniel J. Henderson & Subal C. Kumbhakar, 2011. "When, where and how to perform efficiency estimation," Cologne Graduate School Working Paper Series 02-06, Cologne Graduate School in Management, Economics and Social Sciences.
    • Handle: RePEc:cgr:cgsser:02-06
    as

    Download full text from publisher

    File URL: http://www.cgs.uni-koeln.de/fileadmin/wiso_fak/cgs/pdf/working_paper/cgswp_02-06.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Gong, Byeong-Ho & Sickles, Robin C., 1992. "Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 259-284.
    3. Peter C. Smith & Andrew Street, 2005. "Measuring the efficiency of public services: the limits of analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 401-417, March.
    4. repec:bla:jorssa:v:165:y:2002:i:3:p:405-434 is not listed on IDEAS
    5. Catherine J. Morrison Paul & Warren E. Johnston & Gerald A. G. Frengley, 2000. "Efficiency in New Zealand Sheep and Beef Farming: The Impacts of Regulatory Reform," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 325-337, May.
    6. Schmidt, Peter & Lin, Tsai-Fen, 1984. "Simple tests of alternative specifications in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 24(3), pages 349-361, March.
    7. Berndt, Ernst R. & Morrison, Catherine J., 1995. "High-tech capital formation and economic performance in U.S. manufacturing industries An exploratory analysis," Journal of Econometrics, Elsevier, vol. 65(1), pages 9-43, January.
    8. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2008. "Asymptotics And Consistent Bootstraps For Dea Estimators In Nonparametric Frontier Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1663-1697, December.
    9. 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.
    10. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, University Library of Munich, Germany.
    11. 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.
    12. Park, Soo-Uk & Lesourd, Jean-Baptiste, 2000. "The efficiency of conventional fuel power plants in South Korea: A comparison of parametric and non-parametric approaches," International Journal of Production Economics, Elsevier, vol. 63(1), pages 59-67, January.
    13. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    14. Subal C. Kumbhakar & Efthymios G. Tsionas, 2011. "Stochastic error specification in primal and dual production systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 270-297, March.
    15. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    16. Louis Amato & Christie Amato, 2000. "The Impact of High Tech Production Techniques on Productivity and Profitability in Selected U.S. Manufacturing Industries," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 16(4), pages 327-342, June.
    17. Dassler, Thoralf & Parker, David & Saal, David S., 2006. "Methods and trends of performance benchmarking in UK utility regulation," Utilities Policy, Elsevier, vol. 14(3), pages 166-174, September.
    18. 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.
    19. M. Stone, 2002. "How not to measure the efficiency of public services (and how one might)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 405-434, October.
    20. Wilson, Paul W., 2008. "FEAR: A software package for frontier efficiency analysis with R," Socio-Economic Planning Sciences, Elsevier, vol. 42(4), pages 247-254, December.
    21. KNEIP, Alois & SIMAR, Léopold, 1995. "A General Framework for Frontier Estimation with Panel Data," LIDAM Discussion Papers CORE 1995060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    22. 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.
    23. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    24. Daniel J. Henderson & R. Robert Russell, 2005. "Human Capital And Convergence: A Production-Frontier Approach ," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(4), pages 1167-1205, November.
    25. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    26. Guilkey, David K & Lovell, C A Knox & Sickles, Robin C, 1983. "A Comparison of the Performance of Three Flexible Functional Forms," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(3), pages 591-616, October.
    27. 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.
    28. Cubbin, John & Tzanidakis, George, 1998. "Regression versus data envelopment analysis for efficiency measurement: an application to the England and Wales regulated water industry," Utilities Policy, Elsevier, vol. 7(2), pages 75-85, June.
    29. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
    30. Cubbin, John, 2005. "Efficiency in the water industry," Utilities Policy, Elsevier, vol. 13(4), pages 289-293, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    2. 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.
    3. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.
    4. Mark Andor & Frederik Hesse, "undated". "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    5. Krüger, Jens J., 2012. "A Monte Carlo study of old and new frontier methods for efficiency measurement," European Journal of Operational Research, Elsevier, vol. 222(1), pages 137-148.
    6. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    7. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    8. Jakub Growiec & Anna Pajor & Dorota Gorniak & Artur Predki, 2015. "The shape of aggregate production functions: evidence from estimates of the World Technology Frontier," Bank i Kredyt, Narodowy Bank Polski, vol. 46(4), pages 299-326.
    9. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    10. Oleg Badunenko & Daniel Henderson & R. Russell, 2013. "Polarization of the worldwide distribution of productivity," Journal of Productivity Analysis, Springer, vol. 40(2), pages 153-171, October.
    11. Massimo Finocchiaro Castro & Calogero Guccio & Ilde Rizzo, 2014. "An assessment of the waste effects of corruption on infrastructure provision," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 21(4), pages 813-843, August.
    12. Galina Besstremyannaya, 2013. "The impact of Japanese hospital financing reform on hospital efficiency: A difference-in-difference approach," The Japanese Economic Review, Japanese Economic Association, vol. 64(3), pages 337-362, September.
    13. Isabel Narbón-Perpiñá & Maria Teresa Balaguer-Coll & Marko Petrović & Emili Tortosa-Ausina, 2020. "Which estimator to measure local governments’ cost efficiency? The case of Spanish municipalities," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 11(1), pages 51-82, March.
    14. Julia Schaefer & Marcel Clermont, 2018. "Stochastic non-smooth envelopment of data for multi-dimensional output," Journal of Productivity Analysis, Springer, vol. 50(3), pages 139-154, December.
    15. Thanh Ngo & Kan Wai Hong Tsui, 2022. "Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines," Operational Research, Springer, vol. 22(4), pages 3411-3434, September.
    16. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.
    17. Léopold Simar & Ingrid Keilegom & Valentin Zelenyuk, 2017. "Nonparametric least squares methods for stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 47(3), pages 189-204, June.
    18. Kao, Chiang & Liu, Shiang-Tai, 2009. "Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks," European Journal of Operational Research, Elsevier, vol. 196(1), pages 312-322, July.
    19. David C. Wheelock & Paul W. Wilson, 2009. "Robust, dynamic nonparametric benchmarking: the evolution of cost-productivity and efficiency among U.S. credit unions," Working Papers 2009-008, Federal Reserve Bank of St. Louis.
    20. Collier, Trevor & Johnson, Andrew L. & Ruggiero, John, 2011. "Technical efficiency estimation with multiple inputs and multiple outputs using regression analysis," European Journal of Operational Research, Elsevier, vol. 208(2), pages 153-160, January.

    More about this item

    Keywords

    Bootstrap; Nonparametric Kernel; Technical Efficiency;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cgr:cgsser:02-06. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: David Kusterer (email available below). General contact details of provider: https://edirc.repec.org/data/cgkoede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.