Estimating DEA confidence intervals with statistical panel data analysis
| Autor: | Darold T. Barnum, Surrey M. Walton, Sonali Tandon, Matthew G. Karlaftis, John M. Gleason, Karen L. Shields, Glen T. Schumock |
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| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
Heteroscedasticity Statistical assumption Statistics Econometrics Data envelopment analysis Statistical model Generalized least squares Statistics Probability and Uncertainty Robust confidence intervals CDF-based nonparametric confidence interval Confidence interval Mathematics |
| Zdroj: | Journal of Applied Statistics. 39:815-828 |
| ISSN: | 1360-0532 0266-4763 |
| DOI: | 10.1080/02664763.2011.620948 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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