Download Advanced Robust and Nonparametric Methods in Efficiency by Cinzia Daraio PDF
By Cinzia Daraio
Delivering a scientific and accomplished remedy of modern advancements in potency research, this ebook makes to be had an intuitive but rigorous presentation of complicated nonparametric and powerful equipment, with purposes for the research of economies of scale and scope, trade-offs in construction and repair actions, and causes of potency differentials.
Read Online or Download Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications (Studies in Productivity and Efficiency) PDF
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Extra info for Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications (Studies in Productivity and Efficiency)
Multiple objective approach. A Multiple Criteria Data Envelopment Analysis has been proposed by Li and Reeves (1999) to solve the problems of lack of discrimination and inappropriate weighting schemes in traditional DEA. Extensions to the basic DEA Models Directional distance functions have been introduced by Chambers, Chung and F¨are, (1996) and are based on Luenberger (1992) benefit functions. These functions represent a kind of generalization of the traditional distance functions. Their application leads to measures of technical efficiency from the potential for increasing outputs while reducing inputs at the same time.
For a detailed presentation of FDH concepts see Vanden Eeckaut (1997). Recently, some authors have raised explicit doubts about the economic meaning of FDH, but from the exchange between Thrall (1999) and Cherchye, Kuosmanen and Post (2000), published on the Journal of Productivity Analysis, it 38 The measurement of efficiency emerged that FDH can be economically more meaningful than convex monotone hull, also under non-trivial alternative economic conditions. g. Kuosmanen and Post, 2001, and Cherchye, Kuosmanen and Post, 2001).
This is a consequence of the fact that the efficient frontier is determined by sample observations which are extreme points. Simar (1996) points out the need for identifying and eliminating outliers when using deterministic models. If they cannot be identified, the use of stochastic frontier models is recommended. 5 for an illustration of the influence of outliers in case of FDH estimation. The same is valid for the DEA case. If point A is an extreme point, outlying the cloud of other points, the estimated efficient frontier is strongly influenced by it.