Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation
1993; Institute for Operations Research and the Management Sciences; Volume: 39; Issue: 10 Linguagem: Inglês
10.1287/mnsc.39.10.1265
ISSN1526-5501
Autores Tópico(s)Economic Growth and Productivity
ResumoAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeManagement ScienceVol. 39, No. 10 Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical FoundationRajiv D. BankerRajiv D. BankerPublished Online:1 Oct 1993https://doi.org/10.1287/mnsc.39.10.1265AbstractThis paper provides a formal statistical basis for the efficiency evaluation techniques of data envelopment analysis (DEA). DEA estimators of the best practice monotone increasing and concave production function are shown to be also maximum likelihood estimators if the deviation of actual output from the efficient output is regarded as a stochastic variable with a monotone decreasing probability density function.While the best practice frontier estimator is biased below the theoretical frontier for a finite sample size, the bias approaches zero for large samples. The DEA estimators exhibit the desirable asymptotic property of consistency, and the asymptotic distribution of the DEA estimators of inefficiency deviations is identical to the true distribution of these deviations. This result is then employed to suggest possible statistical tests of hypotheses based on asymptotic distributions. 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