Subsampling bootstrap in network DEA


Data Envelopment Analysis (DEA), provides an empirical estimation of the production frontier, based on an observed sample of decision making units (DMUs). Except for the single input-single output case, the asymptotic distribution of the DEA estimator can only be approximated through bootstrapping approaches. Therefore, bootstrapping techniques have been widely applied in the DEA literature to make statistical inference for the cases when the production process has a single-stage structure. However, in many cases, the transformation of inputs into outputs has an inner structure that needs to be considered. This paper examines the applicability of the subsampling bootstrap procedure in the approximation of the asymptotic distribution of the DEA estimator when the production process has a network structure, and in the presence of undesirable factors. Evidence on the performance of subsampling bootstrap is obtained through Monte Carlo experiments for the case of two-stage series structures, where overall and stage efficiency estimates are calculated using the additive decomposition approach. Results indicate great sensitivity both to the sample and subsample size, as well as to the data generating process. Subsampling methodology is then applied to construct confidence interval estimates for the overall and stage efficiency scores of railways in 22 European countries, where the railway transport process is decomposed into two stages and the railway noise pollution problem is considered as an undesirable output.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (
Uncontrolled Keywords: Data envelopment analysis,Monte carlo,Network efficiency decomposition,Railways,Subsampling bootstrap,Computer Science(all),Modelling and Simulation,Management Science and Operations Research,Information Systems and Management,Industrial and Manufacturing Engineering
Publication ISSN: 1872-6860
Last Modified: 19 Jun 2024 17:48
Date Deposited: 19 Aug 2022 10:26
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 4970?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-03-01
Published Online Date: 2022-06-24
Accepted Date: 2022-06-09
Authors: Michali, Maria
Emrouznejad, Ali
Dehnokhalaji, Akram (ORCID Profile 0000-0002-2751-0719)
Clegg, Ben (ORCID Profile 0000-0001-7506-5237)



Version: Published Version

License: Creative Commons Attribution

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