Benchmarking

Benchmarking: Benchmark and Frontier Analysis Using DEA and SFA. Methods for frontier analysis, Data Envelopment Analysis (DEA), under different technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+), and using different efficiency measures (input based, output based, hyperbolic graph, additive, super, and directional efficiency). Peers and slacks are available, partial price information can be included, and optimal cost, revenue and profit can be calculated. Evaluation of mergers is also supported. Methods for graphing the technology sets are also included. There is also support comparative methods based on Stochastic Frontier Analyses (SFA). In general, the methods can be used to solve not only standard models, but also many other model variants. It complements the book, Bogetoft and Otto, Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of course also be used as a stand-alone package.


References in zbMATH (referenced in 25 articles )

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  1. Musau, Andrew; Kumbhakar, Subal C.; Mydland, Ørjan; Lien, Gudbrand: Determinants of allocative and technical inefficiency in stochastic frontier models: an analysis of Norwegian electricity distribution firms (2021)
  2. Wei, Zheng; Conlon, Erin M.; Wang, Tonghui: Asymmetric dependence in the stochastic frontier model using skew normal copula (2021)
  3. Afsharian, Mohsen; Bogetoft, Peter: Identifying production units with outstanding performance (2020)
  4. Atwood, Joseph; Shaik, Saleem: Theory and statistical properties of quantile data envelopment analysis (2020)
  5. Balk, Bert M.; Barbero, Javier; Zofío, José L.: A toolbox for calculating and decomposing total factor productivity indices (2020)
  6. Bobde, Sudhir Mahadeo; Tanaka, Makoto: Structural reforms and technical efficiency in the Indian electricity distribution sector (2020)
  7. Song, Malin; Wang, Jianlin; Zhao, Jiajia; Baležentis, Tomas; Shen, Zhiyang: Production and safety efficiency evaluation in Chinese coal mines: accident deaths as undesirable output (2020)
  8. Andor, Mark A.; Parmeter, Christopher; Sommer, Stephan: Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes (2019)
  9. Costa, Marcelo Azevedo; Mineti, Leandro Brioschi; Mayrink, Vinícius Diniz; Lopes, Ana Lúcia Miranda: Bayesian detection of clusters in efficiency score maps: an application to Brazilian energy regulation (2019)
  10. Parmeter, Christopher F.; Zelenyuk, Valentin: Combining the virtues of stochastic frontier and data envelopment analysis (2019)
  11. Bjørndal, Endre; Bjørndal, Mette; Cullmann, Astrid; Nieswand, Maria: Finding the right yardstick: regulation of electricity networks under heterogeneous environments (2018)
  12. Bogetoft, Peter; Kromann, Lene: Evaluating treatment effects using data envelopment analysis on matched samples: an analysis of electronic information sharing and firm performance (2018)
  13. Park, SeJoon; Ok, Changsoo; Ha, Chunghun: A stochastic simulation-based holistic evaluation approach with DEA for vendor selection (2018)
  14. Ang, Frederic; Kerstens, Pieter Jan: Decomposing the Luenberger-Hicks-Moorsteen total factor productivity indicator: an application to U.S. agriculture (2017)
  15. Bogetoft, Peter; Hougaard, Jens Leth; Smilgins, Aleksandrs: Applied cost allocation: the DEA-Aumann-Shapley approach (2016)
  16. Gómez-Calvet, Roberto; Conesa, David; Gómez-Calvet, Ana Rosa; Tortosa-Ausina, Emili: On the dynamics of eco-efficiency performance in the European Union (2016)
  17. Wanke, Peter F.; Pestana Barros, Carlos: Evaluating returns to scale and convexity in DEA via bootstrap: a case study with Brazilian port terminals (2016)
  18. Bose, A.; Patel, G. N.: “NeuralDEA” -- a framework using neural network to re-evaluate DEA benchmarks (2015)
  19. Cesaroni, Giovanni; Giovannola, Daniele: Average-cost efficiency and optimal scale sizes in non-parametric analysis (2015)
  20. Tone, K.; Tsutsui, M.: How to deal with non-convex frontiers in data envelopment analysis (2015)

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