Improving Finite Sample Approximation by Central Limit Theorems for Estimates from Data Envelopment Analysis

Leopold Simar and Valentin Zelenyuk

No WP012020, CEPA Working Papers Series from University of Queensland, School of Economics

Abstract: We propose an improvement of the finite sample approximation of the central limit theorems (CLTs) that were recently derived for statistics involving production efficiency scores estimated via Data Envelopment Analysis (DEA) or Free Disposal Hull (FDH) approaches. The improvement is very easy to implement since it involves a simple correction of the variance estimator with an estimate of the bias of the already employed statistics without any additional computational burden and preserves the original asymptotic results such as consistency and asymptotic normality. The proposed approach persistently showed improvement in all the scenarios that we tried in various Monte-Carlo experiments, especially for relatively small samples or relatively large dimensions (measured by total number of inputs and outputs) of the underlying production model. This approach therefore is expected to produce more accurate estimates of confidence intervals of aggregates of individual efficiency scores in empirical research using DEA or FDH approaches and so must be valuable for practitioners. We also illustrate this method using a popular real data set to confirm that the difference in the estimated confidence intervals can be substantial. A step-by-step implementation algorithm of the proposed approach is included in the Appendix.

Keywords: Data Envelopment Analysis, DEA; Free Disposal Hull, FDH; Statistical Inference; Production Efficiency; Productivity. (search for similar items in EconPapers)
Date: 2020-01
New Economics Papers: this item is included in nep-big, nep-ecm and nep-eff
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

Downloads: (external link)
https://economics.uq.edu.au/files/17227/WP012020.pdf (application/pdf)

Related works:
Journal Article: Improving finite sample approximation by central limit theorems for estimates from Data Envelopment Analysis (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qld:uqcepa:144

Access Statistics for this paper

More papers in CEPA Working Papers Series from University of Queensland, School of Economics Contact information at EDIRC.
Bibliographic data for series maintained by SOE IT (soe-it@economics.uq.edu.au).