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Dealing with small samples and dimensionality issues in data envelopment analysis

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  • Zervopoulos, Panagiotis

Abstract

Data Envelopment Analysis (DEA) is a widely applied nonparametric method for comparative evaluation of firms’ efficiency. A deficiency of DEA is that the efficiency scores assigned to each firm are sensitive to sampling variations, particularly when small samples are used. In addition, an upward bias is present due to dimensionality issues when the sample size is limited compared to the number of inputs and output. As a result, in case of small samples, DEA efficiency scores cannot be considered as reliable measures. The DEA Bootstrap addresses this limitation of the DEA method as it provides the efficiency scores with stochastic properties. However, the DEA Bootstrap is still inappropriate in the presence of small samples. In this context, we introduce a new method that draws on random data generation procedures, unlike Bootstrap which is based on resampling, and Monte Carlo simulations.

Suggested Citation

  • Zervopoulos, Panagiotis, 2012. "Dealing with small samples and dimensionality issues in data envelopment analysis," MPRA Paper 39226, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:39226
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Data envelopment analysis; Data generation process; Random data; Bootstrap; Bias correction; Efficiency;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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