Cross Efficiency Evaluation of Decision-Making Units Using the Maximum Decisional Efficiency Principle

This paper uses maximum decisional efficiency (MDE) principle to derive cross efficiency (CE) scores for input and output oriented frontier efficiency models. The MDE CE models are parametric models that derive their cross efficiencies by either maximizing log-likelihood (input-oriented models) or minimizing negative log-likelihood (output-oriented models). Using real-world and simulated datasets, we compare our MDE models with several competing CE models from the literature. Our results illustrate that the MDE models based CE scores have higher CE averages when inputs are independent or correlated with half-normal inefficiency distributions. We also find that MDE models provide model consensus scores that are highly consistent. When inputs are correlated and inefficiency distributions are exponential, the log-likelihood estimation procedures appear to suffer in performance when compared to data envelopment analysis models with secondary objectives.

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Work Title Cross Efficiency Evaluation of Decision-Making Units Using the Maximum Decisional Efficiency Principle
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Penn State
Creators
  1. Parag C. Pendharkar
Keyword
  1. Productivity Analysis, Maximum Likelihood Estimation, Frontier Models, Data Envelopment Analysis
License All rights reserved
Work Type Article
Publication Date May 12, 2020
Deposited February 25, 2021 12:48

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    Publication Date
    • 2020-05-12
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    • https://rightsstatements.org/page/InC/1.0/
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