Estimating DEA confidence intervals with statistical panel data analysis

Darold T. Barnum, John M. Gleason, Matthew G. Karlaftis, Glen T. Schumock, Karen L. Shields, Sonali Tandon and Surrey M. Walton

Journal of Applied Statistics, 2012, vol. 39, issue 4, 815-828

Abstract: This paper describes a statistical method for estimating data envelopment analysis (DEA) score confidence intervals for individual organizations or other entities. This method applies statistical panel data analysis, which provides proven and powerful methodologies for diagnostic testing and for estimation of confidence intervals. DEA scores are tested for violations of the standard statistical assumptions including contemporaneous correlation, serial correlation, heteroskedasticity and the absence of a normal distribution. Generalized least squares statistical models are used to adjust for violations that are present and to estimate valid confidence intervals within which the true efficiency of each individual decision-making unit occurs. This method is illustrated with two sets of panel data, one from large US urban transit systems and the other from a group of US hospital pharmacies.

Date: 2012
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DOI: 10.1080/02664763.2011.620948

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