Dealing with small samples and dimensionality issues in data envelopment analysis
AbstractData 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 39226.
Date of creation: 05 Feb 2012
Date of revision:
Data envelopment analysis; Data generation process; Random data; Bootstrap; Bias correction; Efficiency;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-06-13 (All new papers)
- NEP-ECM-2012-06-13 (Econometrics)
- NEP-EFF-2012-06-13 (Efficiency & Productivity)
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