Combining uncertainty with uncertainty to get certainty? : efficiency analysis for regulation purposes / Mark A. Andor, Christopher Parmeter and Stephan Sommer
VerfasserAndor, Mark Andreas In der Gemeinsamen Normdatei der DNB nachschlagen ; Parmeter, Christopher F. In der Gemeinsamen Normdatei der DNB nachschlagen ; Sommer, Stephan In der Gemeinsamen Normdatei der DNB nachschlagen
ErschienenEssen : RWI - Leibniz-Institut für Wirtschaftsforschung, 2018 ; Bochum : Ruhr-Universität Bochum (RUB), Department of Economics, 2018
Elektronische Ressource
Umfang1 Online-Ressource (91 Seiten) : Diagramme
SerieRuhr economic papers ; #770
SchlagwörterData Envelopment Analysis In Wikipedia suchen nach Data Envelopment Analysis / Frontier-Funktion In Wikipedia suchen nach Frontier-Funktion / Effizienzanalyse In Wikipedia suchen nach Effizienzanalyse / Regulierung In Wikipedia suchen nach Regulierung
URNurn:nbn:de:hbz:6:2-103779 Persistent Identifier (URN)
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Combining uncertainty with uncertainty to get certainty? [1.93 mb]

Data envelopment analysis (DEA) and stochastic frontier analysis (SFA), as well as combinations thereof, are widely applied in incentive regulation practice, where the assessment of efficiency plays a major role in regulation design and benchmarking. Using a Monte Carlo simulation experiment, this paper compares the performance of six alternative methods commonly applied by regulators. Our results demonstrate that combination approaches, such as taking the maximum or the mean over DEA and SFA efficiency scores, have certain practical merits and might offer an useful alternative to strict reliance on a singular method. In particular, the results highlight that taking the maximum not only minimizes the risk of underestimation, but can also improve the precision of efficiency estimation. Based on our results, we give recommendations for the estimation of individual efficiencies for regulation purposes and beyond.