 Central Limit Theorems for Aggregate EfficiencyLeopold Simar and Valentin Zelenyuk Operations Research, 2018, vol. 66, issue 1, 137-149 Abstract: Applied researchers in the field of efficiency and productivity analysis often need to estimate and make inference about aggregate efficiency, such as industry efficiency or aggregate efficiency of a group of distinct firms within an industry (e.g., public versus private firms, regulated versus unregulated firms, etc.). While there are approaches to obtain point estimates for such important measures, no asymptotic theory has been derived for it. This is the gap in the literature we fill with this paper. Specifically, we develop full asymptotic theory for aggregate efficiency measures when the individual true efficiency scores being aggregated are observed as well as when they are unobserved and estimated via the data envelopment analysis or the free disposal hull. As a result, the developed theory opens a path for more accurate and theoretically better grounded statistical inference (e.g., estimation of confidence intervals and conducting statistical tests) on aggregate efficiency estimates such as industry efficiency, etc. Keywords: DEA; FDH; efficiency; aggregation; industry efficiency; asymptotics; limiting distribution; consistency; convergence; jackknife; bias correction (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:66:y:2018:i:1:p:137-149 Access Statistics for this article More articles in Operations Research from INFORMS Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.