Please use this identifier to cite or link to this item:
Nieswand, Maria
Seifert, Stefan
Year of Publication: 
Series/Report no.: 
DIW Discussion Papers 1585
Benchmarking methods are widely used in the regulation of firms in network industries working under heterogeneous exogenous environments. In this paper we compare three recently developed estimators, namely conditional DEA (Daraio and Simar, 2005, 2007b), latent class SFA (Orea and Kumbhakar, 2004; Greene, 2005), and the StoNEZD approach (Johnson and Kuosmanen, 2011) by means of Monte Carlo simulation focusing on their ability to identify production frontiers in the presence of environmental factors. Data generation replicates regulatory data from the energy sector in terms of sample size, sample dispersion and distribution, and correlations of variables. Although results show strengths of each of the three estimators in particular settings, latent class SFA perform best in nearly all simulations. Further, results indicate that the accuracy of the estimators is less sensitive against different distributions of environmental factors, their correlations with inputs, and their impact on the production process, but performance of all approaches deteriorates with increasing noise. For regulators this study provides orientation to adopt new benchmarking methods given industry characteristics.
Monte Carlo Simulation
Environmental Factors
Latent Class SFA
Conditional DEA
Regulatory Benchmarking
Document Type: 
Working Paper

Files in This Item:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.