Dealing with small samples and dimensionality issues in data envelopment analysis

Panagiotis D. Zervopoulos ()

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Data envelopment analysis; Data generation process; Random data; Bootstrap; Bias correction; Efficiency (search for similar items in EconPapers)
JEL-codes: C14 C15 C1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-eff
Date: 2012-02-05
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