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Impact Assessment Of Input Omission On Dea

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Author Info

  • JOHN RUGGIERO

    ()
    (Department of Economics and Finance, School of Business Administration, University of Dayton, Dayton, OH 45469-2251, USA)

Abstract

It is well known in the Data Envelopment Analysis literature that proper variable selection is necessary for the reliable measurement of efficiency. Omitting production relevant variables and/or including irrelevant variables will lead to biased measurement. It is also known that the sample size needs to be large relative to the number of inputs and outputs to prevent classification of efficiency by default. In some empirical settings the number of potential relevant variables is large. Careful selection of an appropriate set of variables is necessary for reliable efficiency measurement. This paper looks at the issue of input selection and uses simulation analysis to develop statistical procedures to provide guidelines for input selection.

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Bibliographic Info

Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal International Journal of Information Technology and Decision Making.

Volume (Year): 04 (2005)
Issue (Month): 03 ()
Pages: 359-368

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Handle: RePEc:wsi:ijitdm:v:04:y:2005:i:03:p:359-368

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Related research

Keywords: Data Envelopment Analysis; variable selection;

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Cited by:
  1. Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2013. "Dealing with the Endogeneity Problem in Data Envelopment Analysis," MPRA Paper 47475, University Library of Munich, Germany.
  2. Tom Broekel & Thomas Brenner, 2007. "Measuring Regional Innovativeness - A Methodological Discussion and an Application to One German Industry," Jena Economic Research Papers 2007-065, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.

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