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A Monte Carlo Evaluation Of Several Tests For The Selection Of Variables In Dea Models

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  • INMACULADA SIRVENT

    (Centro de Investigación Operativa, Universidad Miguel Hernández, Avd. de la Universidad, s/n, Elche, 03202, Alicante, Spain)

  • JOSÉ L. RUIZ

    (Centro de Investigación Operativa, Universidad Miguel Hernández, Avd. de la Universidad, s/n, Elche, 03202, Alicante, Spain)

  • FERNANDO BORRÁS

    (Centro de Investigación Operativa, Universidad Miguel Hernández, Avd. de la Universidad, s/n, Elche, 03202, Alicante, Spain)

  • JESÚS T. PASTOR

    (Centro de Investigación Operativa, Universidad Miguel Hernández, Avd. de la Universidad, s/n, Elche, 03202, Alicante, Spain)

Abstract

Data Envelopment Analysis (DEA) is a recently developed methodology that is widely used for estimating relative efficiency scores of Decision Making Units (DMUs) that use several inputs to produce several outputs. Model specification in DEA includes aspects such as the choice of inputs and outputs or the adoption of a returns to scale assumption. As pointed out by many authors, it is obvious that the specification of a model is the key to having reliable efficiency scores. In this paper, we are particularly concerned with the selection of variables in DEA models. To be specific, we investigate the performance of several statistical tests existing in the literature that can be used for the selection of variables. In particular, the behaviour of the well-known tests proposed by Banker2and the nonparametric tests recently developed by Pastoret al.13is analyzed in relation to several factors such as sample size, model size, the specification of returns to scale and the type and level of inefficiency. We have drawn some conclusions that will be of help for practical uses, since the observed behaviour of the tests in the different scenarios determined by the specifications of the mentioned factors may provide some useful insight into the choice of an adequate statistical test in the particular context of a given DEA application.

Suggested Citation

  • Inmaculada Sirvent & José L. Ruiz & Fernando Borrás & Jesús T. Pastor, 2005. "A Monte Carlo Evaluation Of Several Tests For The Selection Of Variables In Dea Models," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 325-343.
    • Handle: RePEc:wsi:ijitdm:v:04:y:2005:i:03:n:s0219622005001581
    DOI: 10.1142/S0219622005001581
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    Citations

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    Cited by:

    1. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
    2. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, June.
    3. Benítez-Peña, Sandra & Bogetoft, Peter & Romero Morales, Dolores, 2020. "Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach," Omega, Elsevier, vol. 96(C).
    4. Schulte, Hinrich D. & Armbrecht, Linda & Bürger, Rasmus & Gauly, Matthias & Musshoff, Oliver & Hüttel, Silke, 2018. "Let the cows graze: An empirical investigation on the trade-off between efficiency and farm animal welfare in milk production," Land Use Policy, Elsevier, vol. 79(C), pages 375-385.
    5. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    6. Ripoll-Zarraga, Ane Elixabete & Mar-Molinero, Cecilio, 2020. "Exploring the reasons for efficiency in Spanish airports," Transport Policy, Elsevier, vol. 99(C), pages 186-202.
    7. Subhash C. Ray, 2022. "Choice of Inputs and Outputs for Production Analysis," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 26, pages 1083-1116, Springer.
    8. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    9. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    10. Toloo, Mehdi & Tone, Kaoru & Izadikhah, Mohammad, 2023. "Selecting slacks-based data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1302-1318.

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