IDEAS home Printed from https://ideas.repec.org/p/hhs/sdueko/2010_002.html
   My bibliography  Save this paper

Testing over-representation of observations in subsets of a DEA technology

Author

Listed:
  • Asmild, Mette

    (ORMS Group)

  • Hougaard, Jens Leth

    (Department of Operations Management)

  • Olesen, Ole B.

    (Department of Business and Economics)

Abstract

This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test statistic and suggests good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space.

Suggested Citation

  • Asmild, Mette & Hougaard, Jens Leth & Olesen, Ole B., 2010. "Testing over-representation of observations in subsets of a DEA technology," Discussion Papers on Economics 2/2010, University of Southern Denmark, Department of Economics.
    • Handle: RePEc:hhs:sdueko:2010_002
    as

    Download full text from publisher

    File URL: https://www.sdu.dk/-/media/files/om_sdu/institutter/ivoe/disc_papers/disc_2010/dpbe2_2010.pdf
    File Function: Full text
    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. Peter Bogetoft & Jens Hougaard, 2003. "Rational Inefficiencies," Journal of Productivity Analysis, Springer, vol. 20(3), pages 243-271, November.
    3. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    4. 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.
    5. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    6. Wheelock, David C & Wilson, Paul W, 1999. "Technical Progress, Inefficiency, and Productivity Change in U.S. Banking, 1984-1993," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(2), pages 212-234, May.
    7. Nam Anh Tran & Gerald Shively & Paul Preckel, 2010. "A new method for detecting outliers in Data Envelopment Analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 17(4), pages 313-316.
    8. Ole Olesen & N. Petersen, 2003. "Identification and Use of Efficient Faces and Facets in DEA," Journal of Productivity Analysis, Springer, vol. 20(3), pages 323-360, November.
    9. J. Hartigan, 1985. "Statistical theory in clustering," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 63-76, 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. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    2. George E. Halkos & Nickolaos G. Tzeremes, 2015. "Measuring Seaports' Productivity: A Malmquist Productivity Index Decomposition Approach," Journal of Transport Economics and Policy, University of Bath, vol. 49(2), pages 355-376, April.
    3. Pontus Mattsson & Jonas Månsson & Christian Andersson & Fredrik Bonander, 2018. "A bootstrapped Malmquist index applied to Swedish district courts," European Journal of Law and Economics, Springer, vol. 46(1), pages 109-139, August.
    4. Halkos, George & Tzeremes, Nickolaos, 2008. "Measuring regional public health provision," MPRA Paper 23762, University Library of Munich, Germany.
    5. Nadia M. Guerrero & Juan Aparicio & Daniel Valero-Carreras, 2022. "Combining Data Envelopment Analysis and Machine Learning," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    6. 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.
    7. Neves Bezerra de Melo, Felipe Luiz & Sampaio, Raquel Menezes Bezerra & Sampaio, Luciano Menezes Bezerra, 2018. "Efficiency, productivity gains, and the size of Brazilian supermarkets," International Journal of Production Economics, Elsevier, vol. 197(C), pages 99-111.
    8. Halkos, George & Tzeremes, Nickolaos, 2009. "Exploring the effect of countries’ economic prosperity on their biodiversity performance," MPRA Paper 32102, University Library of Munich, Germany.
    9. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
    10. Nitin Arora & Ishfaq Ali Ganaie, 2023. "How Much Technically Efficient Is The South Asian Region? An Intra-Regional Comparison Among Its Affiliates," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 24(1), pages 20-40, March.
    11. Boutheina Bannour & Asma Sghaier & Mohammad Nurunnabi, 2020. "How to Choose a Nonparametric Frontier Model? Technical Efficiency of Turkish Banks Assessing Global," Global Business Review, International Management Institute, vol. 21(2), pages 348-364, April.
    12. He, Haoran & Weng, Qian, 2012. "Ownership, autonomy, incentives and efficiency: Evidence from the forest product processing industry in China," Journal of Forest Economics, Elsevier, vol. 18(3), pages 177-193.
    13. Valero-Carreras, Daniel & Aparicio, Juan & Guerrero, Nadia M., 2021. "Support vector frontiers: A new approach for estimating production functions through support vector machines," Omega, Elsevier, vol. 104(C).
    14. Halkos, George & Tzeremes, Nickolaos, 2011. "A non-parametric analysis of the efficiency of the top European football clubs," MPRA Paper 31173, University Library of Munich, Germany.
    15. George Halkos & Nickolaos Tzeremes, 2010. "The effect of foreign ownership on SMEs performance: An efficiency analysis perspective," Journal of Productivity Analysis, Springer, vol. 34(2), pages 167-180, October.
    16. Fragoudaki, Alexandra & Giokas, Dimitris, 2016. "Airport performance in a tourism receiving country: Evidence from Greece," Journal of Air Transport Management, Elsevier, vol. 52(C), pages 80-89.
    17. Halkos, George & Tzeremes, Nickolaos, 2011. "The effect of national culture on countries’ innovation efficiency," MPRA Paper 30100, University Library of Munich, Germany.
    18. España, Victor J. & Aparicio, Juan & Barber, Xavier & Esteve, Miriam, 2024. "Estimating production functions through additive models based on regression splines," European Journal of Operational Research, Elsevier, vol. 312(2), pages 684-699.
    19. Ioannis E. Tsolas, 2023. "Efficiency Measurement of Lignite-Fired Power Plants in Greece Using a DEA-Bootstrap Approach," Sustainability, MDPI, vol. 15(4), pages 1-10, February.
    20. Bampatsou, Christina & Halkos, George, 2018. "Dynamics of productivity taking into consideration the impact of energy consumption and environmental degradation," Energy Policy, Elsevier, vol. 120(C), pages 276-283.

    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Over-representation; Data density; Binomial test; Convex hull;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

    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:hhs:sdueko:2010_002. 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: Astrid Holm Nielsen (email available below). General contact details of provider: https://edirc.repec.org/data/okioudk.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.