DATA ENVELOPMENT ANALYSIS and Confident-DEA

This work contributes to the Data Envelopment Analysis (DEA) literature at three ways. First, it extends the roots of DEA by providing an analytical approach deriving the basic Charnes- Cooper-Rhodes (1978) model from the Weak Axiom of Profit Maximization (WAPM) of Firm Theory. Second, this work provides a systematic way for classifying the existing DEA literature by offering a taxonomy. Finally, a theoretical contribution to the literature, Confident-DEA approach, is proposed involving a bilevel convex optimization model to which a Genetic-Algorithm-based solution method is suggested. Complementing previous DEA methodologies, which provides single valued efficiency measures, Confident-DEA provides a range of values for the efficiency measures, an efficiency confidence interval and hence the name, reflecting the imprecision in data. Monte-Carlo simulation is used to determine the distribution of the efficiency measures, taking into account the distribution of the bounded imprecise data over their corresponding intervals. Confident-DEA is applied to predict the efficiency of banking systems in OECD countries.