Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes

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dc.contributor.authorAndor, Mark
dc.contributor.authorParmeter, Christopher
dc.contributor.authorSommer, Stephan
dc.date.accessioned2018-10-10T13:17:53Z
dc.date.available2018-10-10T13:17:53Z
dc.date.issued2018
dc.description.abstractData 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.en
dc.identifier.urihttp://hdl.handle.net/2003/37146
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-19142
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;23/2018en
dc.subjectdata envelopment analysisen
dc.subjectnetwork operatorsen
dc.subjectregulationen
dc.subjectefficiency analysisen
dc.subjectstochastic frontier analysisen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkData Envelopment Analysisde
dc.subject.rswkFrontier-Funktionde
dc.subject.rswkEffizienzanalysede
dc.subject.rswkRegulierungde
dc.titleCombining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposesen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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