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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/26263

Title: Environmental factors in frontier estimation - A Monte Carlo analysis
Authors: Nieswand, Maria
Seifert, Stefan
Keywords: Monte Carlo simulation
Environmental factors
Conditional DEA
Latent class SFA
Issue Date: 2018
Publisher: © Elsevier
Citation: 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.
Abstract: 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.
Description: This paper is closed access until 21st July 2019
Sponsor: 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.
Version: Accepted for publication
DOI: 10.1016/j.ejor.2017.07.047
URI: https://dspace.lboro.ac.uk/2134/26263
Publisher Link: http://dx.doi.org/10.1016/j.ejor.2017.07.047
ISSN: 0377-2217
Appears in Collections:Closed Access (Economics)

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