Maximum entropy: a stochastic frontier approach for electricity distribution regulation

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Isabel Soares (Author) FEP; Elvira Silva (Author) FEP; Pedro Macedo (Author) Other

Journal

Title: Journal of Regulatory Economics

Vol. 55

Pages: 237-257

ISSN: 0922-680X

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 8 Citations

Scopus - 10 Citations

Other information

Authenticus ID: P-00Q-NW1

DOI: 10.1007/s11149-019-09383-y

Abstract (EN): The literature on incentive-based regulation in the electricity sector indicates that the size of this sector in a country constrains the choice of frontier methods as well as the model specification itself to measure economic efficiency of regulated firms. The aim of this study is to propose a stochastic frontier approach with maximum entropy estimation, which is designed to extract information from limited and noisy data with minimal statements on the data generation process. Stochastic frontier analysis with generalized maximum entropy and data envelopment analysis-the latter one has been widely used by national regulators-are applied to a cross-section data on thirteen European electricity distribution companies. Technical efficiency scores and rankings of the distribution companies generated by both approaches are sensitive to model specification. Nevertheless, the stochastic frontier analysis with generalized maximum entropy results indicate that technical efficiency scores have similar distributional properties and these scores as well as the rankings of the companies are not very sensitive to the prior information. In general, the same electricity distribution companies are found to be in the highest and lowest efficient groups, reflecting weak sensitivity to the prior information considered in the estimation procedure.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21