Inference for aggregate efficiency: Theory and guidelines for practitioners

Leopold Simar, Valentin Zelenyuk and Shirong Zhao

No 2024012, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: We expand and develop further the recently developed framework for the inference about the aggregate efficiency, extending the existing theory and providing guidelines for practitioners. In Monte Carlo simulations, we thoroughly examine the performance of the various improvement methods (compared with the original CLT results) for the aggregate input-oriented and output-oriented efficiency for different ranges of small samples and different dimensions of the production model. From the simulations, we conclude that: (i) when the sample sizes are relatively small (around 200 and less), the full variance correction method (adapted from Simar et al., 2023) with the data sharpening method (adapted from Nguyen et al., 2022) generally provides a better performance; (ii) when the sample sizes are relatively large, the full variance correction method without the data sharpening method is expected to perform better than the other suitable methods known to date. Finally, we use two well-known empirical data sets to illustrate the practical implementations and the differences across the existing methods to facilitate their use by practitioners.

Keywords: Data envelopment analysis; Efficiency; Non-parametric efficiency estimators; Free disposal hull; Aggregate efficiency (search for similar items in EconPapers)
Pages: 15
Date: 2024-02-01
Note: In: European Journal of Operational Research, 2024, vol. 316 (1), p. 240-254
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Inference for aggregate efficiency: Theory and guidelines for practitioners (2024)
Working Paper: Inference for Aggregate Efficiency: Theory and Guidelines for Practitioners (2023)
Working Paper: Inference for Aggregate Efficiency: Theory and Guidelines for Practitioners (2023)
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:aiz:louvar:2024012

DOI: 10.1016/j.ejor.2024.01.028

Access Statistics for this paper

More papers in LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) Voie du Roman Pays 20, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Nadja Peiffer ().