IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v9y1998i2p161-176.html
   My bibliography  Save this article

Another Approach to Data Envelopment Analysis in Noisy Environments: DEA+

Author

Listed:
  • Dieter Gstach

Abstract

In this paper a DEA+ labeled approach for efficiency measurement in the stochastic case is presented along with a consistency proof and some preliminary evidence illustrating the small sample performance. DEA+ can basically handle multi-output technologies like standard DEA but allows to filter noise, that might have disturbed production and unlike a related approach does not require panel data. Consistency of DEA+ relies on the assumption of i.i.d. distributed and bounded noise and requires radial efficiency measurement. First Monte Carlo experiments show that a DEA+ based average inefficiency estimator performs well for samples of size n=100 in one-output, two-input settings compared to the corresponding Stochastic Frontier Estimator. Sensitivity of DEA+ performance with respect to parametrization of noise is weak, but higher noise contribution requires much larger sample size for satisfactory results. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Dieter Gstach, 1998. "Another Approach to Data Envelopment Analysis in Noisy Environments: DEA+," Journal of Productivity Analysis, Springer, vol. 9(2), pages 161-176, March.
    • Handle: RePEc:kap:jproda:v:9:y:1998:i:2:p:161-176
    DOI: 10.1023/A:1018312801700
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018312801700
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018312801700?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gstach, Dieter, 1995. "Comparing Structural Efficiency of Unbalanced Subsamples: A Resampling Adaptation of Data Envelopment Analysis," Empirical Economics, Springer, vol. 20(3), pages 531-542.
    2. 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.
    3. Kittelsen,S.A.C., 1999. "Monte Carlo simulations of DEA efficiency measures and hypothesis tests," Memorandum 09/1999, Oslo University, Department of Economics.
    4. 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.
    5. Patrick L. Brockett & Boaz Golany, 1996. "Using Rank Statistics for Determining Programmatic Efficiency Differences in Data Envelopment Analysis," Management Science, INFORMS, vol. 42(3), pages 466-472, March.
    6. 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.
    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, March.
    2. Alessandra Cepparulo & Gilles Mourre, 2020. "How and How Much? The Growth-Friendliness of Public Spending through the Lens," European Economy - Discussion Papers 132, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    3. Chin‐wei Huang & Hsiao‐Yin Chen, 2023. "Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1904-1919, June.
    4. 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.
    5. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    6. repec:agr:journl:v:4(621):y:2019:i:4(621):p:241-264 is not listed on IDEAS
    7. Cristian Barra & Roberto Zotti, 2016. "Measuring Efficiency in Higher Education: An Empirical Study Using a Bootstrapped Data Envelopment Analysis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 22(1), pages 11-33, February.
    8. Michael Zschille & Matthias Walter, 2012. "The performance of German water utilities: a (semi)-parametric analysis," Applied Economics, Taylor & Francis Journals, vol. 44(29), pages 3749-3764, October.
    9. Shaher Z. Zahran & Jobair Bin Alam & Abdulrahem H. Al-Zahrani & Yiannis Smirlis & Stratos Papadimitriou & Vangelis Tsioumas, 2020. "Analysis of port efficiency using imprecise and incomplete data," Operational Research, Springer, vol. 20(1), pages 219-246, March.
    10. Ivonne Lindlbauer & Jonas Schreyögg, 2014. "The relationship between hospital specialization and hospital efficiency: do different measures of specialization lead to different results?," Health Care Management Science, Springer, vol. 17(4), pages 365-378, December.
    11. Carlos Pestana Barros & Gaël Bertrand & Laurent Botti & Scott Tainsky, 2014. "Cost efficiency of French rugby clubs," Applied Economics, Taylor & Francis Journals, vol. 46(23), pages 2721-2732, August.
    12. Kjoeserud,G.G. & Kvamme,O.J. & Kittelsen,S.A.C., 2001. "Errors in survey based quality evaluation variables in efficiency models of primary care physicians," Memorandum 24/2001, Oslo University, Department of Economics.
    13. Alexandre Marinho & Simone de Souza Cardoso & Vivian Vicente de Almeida, 2009. "Brasil e OCDE: Avaliação da Eficiência em Sistemas de Saúde," Discussion Papers 1370, Instituto de Pesquisa Econômica Aplicada - IPEA.
    14. Danish Ahmed SIDDIQUI & Qazi Masood AHMED, 2019. "Exploring the role of institutions in cross country Malmquist productivity analysis: A two-stage double bootstrap DEA approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 241-264, Winter.
    15. Carlos Rosano-Peña & João Vitor Borges Silva & André Luiz Marques Serrano & José Eustáquio Ribeiro Vieira Filho & Herbert Kimura, 2022. "Eco-Efficiency of Agriculture in the Amazon Biome: Robust Indices and Determinants," World, MDPI, vol. 3(4), pages 1-19, September.
    16. Kittelsen, Sverre A.C. & Magnussen, Jon, 2009. "Testing DEA Models of Efficiency in Norwegian Psychiatric Outpatient Clinics," HERO Online Working Paper Series 1999:4, University of Oslo, Health Economics Research Programme.
    17. 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.
    18. Jesús T. Pastor & JosÉ L. Ruiz & Inmaculada Sirvent, 2002. "A Statistical Test for Nested Radial Dea Models," Operations Research, INFORMS, vol. 50(4), pages 728-735, August.
    19. Franz R. Hahn, 2005. "Determinants of Bank Profitability in Austria. A Micro-Macro Approach," WIFO Studies, WIFO, number 25688, March.
    20. Montri Singhavara & Nisachon Leerattanakorn & Aree Cheamuangphan & Kamontip Panyasit, 2013. "An analysis of operational efficiency and optimal development for agricultural cooperatives in Chiang Mai," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 2(2), pages 83-96, June.
    21. Dieter Gstach, 1996. "A new approach to stochastic frontier estimation: DEA+," Department of Economics Working Papers wuwp039, Vienna University of Economics and Business, Department of Economics.

    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:kap:jproda:v:9:y:1998:i:2:p:161-176. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.