Data Envelopment Analysis and Confident-dea: a Taxonomy, a Meta Review and an Extension, with Application to Predicting Cross (Oecd) Country Banking Systems¿ Efficiency

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.