Multiple Comparisons with the Best: Bayesian Precision Measures of Efficiency Rankings

Scott Atkinson and Jeffrey Dorfman ()

Journal of Productivity Analysis, 2005, vol. 23, issue 3, 359-382

Abstract: A large literature measures the allocative and technical efficiency of a set of firms using econometric techniques to estimate stochastic production frontiers or distance functions. Typically, researchers compute only the precision of individual efficiency rankings. Recently, Horrace and Schmidt (Journal of Applied Economics 15, 1–26, 2000) have applied sampling theoretic statistical techniques known as multiple comparisons with a control (MCC) and multiple comparisons with the best (MCB) to make statistical comparisons of efficiency rankings. As an alternative, this paper offers a Bayesian multiple comparison procedure that we argue is simpler to implement, gives the researcher increased flexibility over the type of comparison, and provides greater, and more intuitive, information content. For these methods and a parametric bootstrap technique, we carry out multiple comparisons of technical efficiency rankings for a set of U.S. electric generating firms, estimated using a distance function framework. We find that the Bayesian method provides substantially more precise inferences than obtained using the MCB and MCC methods. Copyright Springer Science+Business Media, Inc. 2005

Keywords: distance functions; electric utilities; Gibbs sampling; technical efficiency rankings; electric utilities; multiple comparisons with the best (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (7)

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Working Paper: MULTIPLE COMPARISONS WITH THE BEST: BAYESIAN PRECISION MEASURES OF EFFICIENCY RANKINGS (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:23:y:2005:i:3:p:359-382

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DOI: 10.1007/s11123-005-2215-9

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