Abstract
Data envelopment analysis (DEA), a non-parametric efficiency estimator uses linear programming technique for the computation of estimates of decision-making units, such as, universities, schools, hospitals, banks, or mutual funds. There has been an ongoing debate about the application of the DEA model for model misspecification and in particular the inefficiency error of production for input and output variables. This paper contributes to this debate by examining several misspecifications of the DEA model in Monte Carlo (MC) simulations. MC simulations are conducted to examine the performance of the DEA model under two different data generating processes, stochastic and deterministic, and across five different misspecification scenarios, inefficiency distributions (traditional and proposed approaches), sample sizes, production functions, input distributions, and curse of dimensionality.
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