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A Bayesian approach for correcting bias of data envelopment analysis estimators

Zervopoulos, Panagiotis and Emrouznejad, Ali and Sklavos, Sokratis (2019): A Bayesian approach for correcting bias of data envelopment analysis estimators.

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Abstract

The validity of data envelopment analysis (DEA) efficiency estimators depends on the robustness of the production frontier to measurement errors, specification errors and the dimension of the input-output space. It has been proven that DEA estimators, within the interval (0, 1], are overestimated when finite samples are used while asymptotically this bias reduces to zero. The non-parametric literature dealing with bias correction of efficiencies solely refers to estimators that do not exceed one. We prove that efficiency estimators, both lower and higher than one, are biased. A Bayesian DEA method is developed to correct bias of efficiency estimators. This is a two-stage procedure of super-efficiency DEA followed by a Bayesian approach relying on consistent efficiency estimators. This method is applicable to ‘small’ and ‘medium’ samples. The new Bayesian DEA method is applied to two data sets of 50 and 100 E.U. banks. The mean square error, root mean square error and mean absolute error of the new method reduce as the sample size increases.

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