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  • Broschiertes Buch

Economic efficiency analysis has received considerable worldwide attention in the last few decades, with Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) establishing themselves as the two dominant approaches in the literature. This book, by combining cutting-edge theoretical research on DEA and SFA with attractive real-world applications, offers a valuable asset for professors, students, researchers, and professionals working in all branches of economic efficiency analysis, as well as those concerned with the corresponding economic policies.
The book is divided into
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Produktbeschreibung
Economic efficiency analysis has received considerable worldwide attention in the last few decades, with Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) establishing themselves as the two dominant approaches in the literature. This book, by combining cutting-edge theoretical research on DEA and SFA with attractive real-world applications, offers a valuable asset for professors, students, researchers, and professionals working in all branches of economic efficiency analysis, as well as those concerned with the corresponding economic policies.

The book is divided into three parts, the first of which is devoted to basic concepts, making the content self-contained. The second is devoted to DEA, and the third to SFA. The topics covered in Part 2 range from stochastic DEA to multidirectional dynamic inefficiency analysis, including directional distance functions, the elimination and choice translating algorithm, benefit-of-the-doubt composite indicators,and internal benchmarking for efficiency evaluations. Part 3 also includes exciting and cutting-edge theoretical research on e.g. robustness, nonparametric stochastic frontier models, hierarchical panel data models, and estimation methods like corrected ordinary least squares and maximum entropy.

Autorenporträt
Pedro Macedo is an assistant professor in the Department of Mathematics at University of Aveiro (DMat-UA, Portugal) and a member of the Center for Research & Development in Mathematics and Applications (CIDMA, Portugal). He holds a PhD in Mathematics (Probability and Statistics), a MSc in Economics, and a licentiate degree in Applied Mathematics. His research interests lie in info-metrics, large-scale data, maximum entropy, productivity and efficiency analysis, and shrinkage estimation.  Victor Manuel Ferreira Moutinho has a PhD in Energy Systems and Climate Change. He published several articles in the following journals: Energy Policy, Utilities Policy, Empirical Economics, Renewable and Sustainable Energy Reviews, Journal of Cleaner Production, Environmental Science and Pollution Research, Environment Development and Sustainability, Energy-International Journal, Agricultural Economics, Energy Sources, Part B: Economics, Planning and Policy, Energy Procedia, InternationalJournal of Energy Economics and Policy, International Journal of Energy Technology and Policy. Currently, he is Director of the master in economics at University of Beira Interior (UBI). He is Coordinator of Economics and Finance and Full Researcher at NECE - Research Center in Business Sciences at the UBI, Associate Editor at Environmental Development and Sustainability (Springer), Academic Editor at Energies - MPDI and Academic Editor at The PLOS ONE. Mara Madaleno is an assistant professor in the Department of Economics, Management, Industrial Engineering and Tourism at the University of Aveiro (DEGEIT-UA, Portugal) and a member of the Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP, Portugal). Currently, she is the Director of the Master in Economics, Vice-director of the Master in Data Science for Social Sciences, and Vice-coordinator of the GOVCOPP research group Systems for Decision Support (SDS). She holds a PhD in Economics (Financial Economics), and a licentiate degree in Economics. Her research interests lie in financial economics, financial markets, energy and environmental financial markets, and financial markets/assets efficiency.