Abstract
This research proposes Quantile Data Envelopment Analysis (qDEA) as a procedure that accounts for the sensitivity of Data Envelopment Analysis (DEA) to data or firm outliers when using DEA to estimate comparative efficiency or benchmarking performance metrics. The qDEA methodology endogenously identifies the distance to a qDEA-α hyperplane while allowing up to proportion q = 1 - α of the data observations to lie external to the qDEA-α hyperplane. The ability of qDEA to provide more conventional quantile-based benchmarking information is discussed. The statistical properties of the qDEA estimator are examined utilizing nCm subsampling and Monte Carlo procedures. Monte Carlo simulations indicate that qDEA distance estimates share the desirable root-n convergence and large sample normality properties of the robust Free Disposal Hull (FDH) based order-m and order-α estimators.
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