Nieswand_Seifert_2017_EJOR_accepted_manuscript.pdf (960.92 kB)
Environmental factors in frontier estimation - A Monte Carlo analysis
journal contributionposted on 2017-08-30, 10:51 authored by Maria Nieswand, Stefan Seifert
We 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.
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.
- Business and Economics
Published inEuropean Journal of Operational Research
Pages133 - 148
CitationNIESWAND, M. and SEIFERT, S., 2018. Environmental factors in frontier estimation - A Monte Carlo analysis. European Journal of Operational Research, 265(1), pp. 133-148.
- AM (Accepted Manuscript)
Rights holder© Elsevier
Publisher statementThis 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/
NotesThis 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.