Overcoming Data Limitations in Nonparametric Benchmarking: Applying PCA-DEA to Natural Gas Transmission

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz-bzw. Nationallizenz frei zugänglich. -This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively. Terms of use: Documents inOvercoming Data Limitations in Nonparametric Benchmarking: Applying PCA-DEA to Natural Gas Transmission Maria Nieswand, Astrid Cullmann, and Anne Neumann AbstractWe empirically demonstrate a practical approach of efficiency evaluation with limited data availability in some regulated industries. We apply PCA-DEA for radial efficiency measurement to U.S. natural gas transmission companies in 2007. PCA-DEA reduces dimensions of the optimization problem while maintaining most of the variation in the original data. Our results suggest that the methodology reduces the probability of over-estimation of individual firm-specific performance.

Efficiency Evaluation in Search Advertising

“…There are two main streams of efficiency evaluation and benchmarking approaches in the literature: nonparametric and parametric. DEA as a nonparametric approach and stochastic frontier analysis (SFA) as a parametric approach are the most commonly used methods (Nieswand, Cullmann, & Neumann, 2010). DEA is a nonparametric method used to empirically measure the relative efficiency of decision making units (DMUs) that use multiple inputs to produce multiple outputs (Charnes, Cooper, & Rhodes, 1978).…”

“…DEA is a nonparametric method used to empirically measure the relative efficiency of decision making units (DMUs) that use multiple inputs to produce multiple outputs (Charnes, Cooper, & Rhodes, 1978). As a nonparametric method, DEA's reference technology is not determined by imposing a functional form that describes the production process, but by means of linear programming methods assuming a transformation of inputs into outputs (Nieswand et al, 2010). The basic DEA models consider two types of technology, constant returns to scale (CRS) proposed by Charnes et al (1978), and variable returns to scale (VRS) suggested by Banker, Charnes, and Cooper (1984).…”

A Nonparametric Economic Analysis of the US Natural Gas Transmission Infrastructure: Efficiency, Trade-Offs and Emerging Industry Configurations

“…The super-efficiency model is utilized to estimate efficiency of 25 Iranian gas companies. Nieswand et al [19] employ PCA-DEA to measure the efficiency of 37 US natural gas transmission companies in 2007. Particularly, they implement two model settings which include the same cost measurement but differ in the number of cost drivers under the assumption of variable returns to scale technology.…”