[:es]Jradi, S. (Kedge Business School, Talence, 680 Cours Libération 33405, France); Bouzdine Chameeva, T. (Kedge Business School, Talence, 680 Cours Libération 33405, France); Aparicio, J. (Center of Operations Research (CIO), University Miguel Hernandez of Elche (UMH), Elche, Alicante 03202, Spain)
Abstract. In this paper, we measure and decompose revenue inefficiency over time while accounting for all sources of technical inefficiencies. Our proposed decomposition exploits the dual relationship between the weighted additive distance function and revenue inefficiency in Aparicio et al. [1]. With the aid of the Luenberger indicator, we decompose this indicator into productivity change, and overall allocative change components. The importance of such decomposition is that it provides a complete picture of the sources of productivity change, thus obtaining a slack free allocative component. Finally, the model is ap- plied to the French wine sector to illustrate its practicality: we track how revenue inefficiency evolves in French wine regions over the 2004–2013 period, before and after the implementation of Common Market Organization policies in Europe in 2008.[:]
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Seminario CIO-Prometeo: Predicting chaotic dynamics with reservoir computing
Título: Predicting chaotic dynamics with reservoir computing Ponente: Ulrich Parlitz (Max Planck Institute for Dynamics and Self-Organization) Fecha y hora: 04/12/2024, 12:00 Inscripción online (cierre 30 minutos antes del inicio): https://forms.gle/dvaxftX815mttHvN9 Lugar: Sala de Seminarios del Edificio Torretamarit (CIO) y online Organizador: José María Amigó García Abstract: Reservoir computing utilizes the response of driven dynamical systems for predicting and analyzing time [...]
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