Consequences of omitting relevant inputs on the quality of the data envelopment analysis under different input correlation structures

dc.contributor.authorRamírez Hassan, Andrés
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.emailaramir21@eafit.edu.co.spa
dc.date.accessioned2013-03-08T18:58:22Z
dc.date.available2013-03-08
dc.date.available2013-03-08T18:58:22Z
dc.date.issued2008-02-03
dc.description.abstractThis paper establishes the consequences of a wrong specification on the quality of the data envelopment analysis. Specifically, the case of omitting a relevant variable in the input oriented problem is analyzed when there are different correlation structures between the inputs. It is established that the correlation matrix gives relevant information about the homogeneity of the decision making units and the intensity of inputs used in the production process. The methodology is based on a series of Monte Carlo simulations and the quality of the data envelopment analysis is measured as the difference between the true efficiency and the efficiency calculated. It is found that omitting relevant inputs causes inconsistency, and this problem is worse when there is a negative correlation structure.eng
dc.identifier.urihttp://hdl.handle.net/10784/556
dc.language.isoengeng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.localAcceso abiertospa
dc.subject.keywordEfficiencyeng
dc.subject.keywordData Envelopment Analysiseng
dc.subject.keywordMonte Carlo Simulationeng
dc.subject.keywordInput Correlation Structureeng
dc.titleConsequences of omitting relevant inputs on the quality of the data envelopment analysis under different input correlation structureseng
dc.typeworkingPapereng
dc.typeinfo:eu-repo/semantics/workingPapereng
dc.type.hasVersiondrafteng
dc.type.localDocumento de trabajo de investigaciónspa

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