Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector
| Autor: | Kekoura Sakouvogui, Saleem Shaik, Kwame Asiam Addey |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Nonparametric tests
Wilcoxon signed-rank test banking 0211 other engineering and technologies 02 engineering and technology Efficiency Analysis Cluster analysis A10 Statistics ddc:330 0202 electrical engineering electronic engineering information engineering Data envelopment analysis nonparametric tests g21 C14 C10 HB71-74 Statistic Mathematics c44 021103 operations research c14’ Kruskal–Wallis one-way analysis of variance Nonparametric statistics Banking Data point a10 Economics as a science efficiency analysis G21 020201 artificial intelligence & image processing c10 C44 Panel data cluster analysis |
| Zdroj: | Open Economics, Vol 3, Iss 1, Pp 50-69 (2020) |
| ISSN: | 2451-3458 |
| Popis: | This paper improves on the issues of extreme data points and heterogeneity found in the linear programming data envelopment analysis (DEA) by presenting a cluster-adjusted DEA model (DEA with cluster approach). This analysis, based on efficiency, determines the number of clusters via Gap statistic and Elbow methods. We use the December quarterly panel data consisting of 122 U.S agricultural banks across 37 states from 2000 to 2017 to estimate the cluster-adjusted DEA model. Empirical results show differences in the estimated DEA efficiency measures with and without a clustering approach. Furthermore, using non-parametric tests, the results of Ansari-Bradley, Kruskal Wallis, and Wilcoxon Rank Sum tests suggest that the cluster-adjusted DEA model provides statistically better efficiency measures in comparison to the DEA model without a clustering approach. |
| Databáze: | OpenAIRE |
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