Nieswand_Seifert_2017_EJOR_accepted_manuscript.pdf (960.92 kB)
Environmental factors in frontier estimation - A Monte Carlo analysis
journal contribution
posted on 2017-08-30, 10:51 authored by Maria Nieswand, Stefan SeifertWe compare three recently developed frontier estimators, namely the conditional DEA (Daraio and Simar, 2005; 2007b), the latent class SFA (Greene, 2005; Orea and Kumbhakar, 2004), and the StoNEZD approach (Johnson and Kuosmanen, 2011) by means of Monte Carlo simulation. We focus on their ability to identify production frontiers and efficiency rankings in the presence of environmental factors. Our simulations match features of real life datasets and cover a wide range of scenarios with variations in sample size, distribution of noise and inefficiency, as well as in distributions, intensity, and number of environmental variables. Our results provide insight in the finite sample properties of the estimators, while also identifying estimator-specific characteristics. Overall, the latent class approach is found to perform best, although in many cases StoNEZD shows a similar performance. Performance of cDEA is most often inferior.
Funding
This paper is partly produced as part of the KOMIED (Municipal infrastructure companies against the background of energy policy and demographic change) financed by Leibniz Association.
History
School
- Business and Economics
Department
- Economics
Published in
European Journal of Operational ResearchVolume
265Issue
1Pages
133 - 148Citation
NIESWAND, M. and SEIFERT, S., 2018. Environmental factors in frontier estimation - A Monte Carlo analysis. European Journal of Operational Research, 265(1), pp. 133-148.Publisher
ElsevierVersion
- AM (Accepted Manuscript)
Rights holder
© ElsevierPublisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-07-14Publication date
2017-07-21Notes
This paper was accepted for publication in the journal European Journal of Operational Research and the definitive published version is available at https://doi.org/10.1016/j.ejor.2017.07.047.ISSN
0377-2217Publisher version
Language
- en