Bias-correction in DEA efficiency scores using simulated beta samples: an alternative view of bootstrapping in DEA (Q2204608)

Summary: Bootstrapping of DEA efficiency scores came into being under the criticism that DEA input/output data may contain random error, and as a result the efficient frontier may be warped by statistical noise. Since the publication of the seminal paper by \textit{L. Simar} and \textit{P. W. Wilson} [Manage. Sci. 44, No. 1, 49--61 (1998; Zbl 1012.62501)], several researchers have carried out bootstrapping the DEA frontier, re-computing the efficiency scores after correcting the biases and developing confidence intervals for bias-corrected scores. We view bias-correction in DEA efficiency scores from a different perspective by randomising the efficiency scores that follow underlying beta distributions. In a step-by-step process, using the simulated beta samples, we show how to correct the biases of individual scores, construct confidence intervals for the bias-corrected mean scores and derive some statistical results for the estimators used in the process. Finally, we demonstrate this method by applying it to a set of banks.