Constructions of economic functions and calculations of marginal rates in DEA using parametric optimization methods

Krivonozhko, Vladimir E.; Utkin, Andrei B.; Volodin, A. V.; Sablin, I. A.; Patrin, M. V.

doi:10.1057/palgrave.jors.2601759

Cited by 65 publications

(32 citation statements)

References 12 publications

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“…Because the production frontiers of these technologies are not explicitly known and are not smooth, the analysis of their marginal rates and elasticities is not straightforward. As noted by many researchers (Charnes et al 1985, Banker at Maindiratta 1986, Olesen and Petersen 1996, Krivonozhko et al 2004), earlier results on marginal rates and scale elasticity were obtained only for relative interior points of efficient facets of the technology and were not rigorously proved at the extreme points corresponding to observed efficient units.…”

Section: Closely Related To the Latter Is The Characterization Of Retmentioning

confidence: 95%

Marginal Values and Returns to Scale for Nonparametric Production Frontiers

Podinovski

Chambers

Atıcı

et al. 2016

Operations Research

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We present a unifying linear programming approach to the calculation of various directional derivatives for a very large class of production frontiers of data envelopment analysis (DEA). Special cases of this include different marginal rates, the scale elasticity and a spectrum of partial and mixed elasticity measures. Our development applies to any polyhedral production technology including, to name a few, the conventional variable and constant returns-to-scale DEA technologies, their extensions with weight restrictions, technologies with weakly disposable undesirable outputs and network DEA models. Furthermore, our development provides a general method for the characterization of returns to scale (RTS) in any polyhedral technology.The new approach effectively removes the need to develop bespoke models for the RTS characterization and calculation of marginal rates and elasticity measures for each particular technology.

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Section: Closely Related To the Latter Is The Characterization Of Retmentioning

confidence: 95%

Marginal Values and Returns to Scale for Nonparametric Production Frontiers

Podinovski

Chambers

Atıcı

et al. 2016

Operations Research

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“…Krivonozhko et al (2005) have proved that set T WEff P coincides with the boundary of B T (2). Krivonozhko et al (2004) and Volodin et al (2004).…”

Section: Fig 8 Transformation Of the Frontier With The Help Of Artimentioning

confidence: 94%

Terminal units in DEA: definition and determination

2013

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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. Abstract: Applications of the DEA models show that inadequate results may arise in some cases, two of these inadequacies being: a) too many efficient units may appear in some DEA models; b) a DEA model may show an inefficient unit from the point of view of experts as an efficient one. The purpose of this paper is to identify units that may unduly become efficient. The concept of a terminal unit is introduced for such units. A method for improving the adequacy of DEA models based on terminal units is suggested, and an example shown based on a real-life data set for Russian banks. Terms of use: Documents in

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“…A two-dimensional projection is then defined relative to a particular point of the technology. For example, Krivonozhko, Utkin, Volodin, Sablin, and Patrin (2004) offers parametric optimization tools to reconstruct an intersection of the multidimensional convex production frontier with a two-dimensional plane determined by any pair of given directions. We simply adapt the same idea to a nonconvex technology.…”

Section: Production Frontiersmentioning

confidence: 99%

Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration

Cesaroni¹,

Kerstens

Woestyne

2017

European Journal of Operational Research

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The purpose of this contribution is to empirically implement and supplement the proposals made by Podinovski (2004b) to explore the nature of both global and local returns to scale in nonconvex nonparametric technologies. In particular, we both propose a simplified method to compute the global returns to scale and employ some secondary data sets to investigate the frequency of the special case of global sub-constant returns to scale. Furthermore, when determining global returns to scale using both convex and nonconvex technologies, we verify how often the resulting information is concordant or conflicting. Finally, besides comparing the FDH and DEA evolution of ray-average productivity for some typical individual observations, we introduce in the literature two original methods for the determination of local returns to scale in non-convex technologies. * We acknowledge comments made by participants in the North American Productivity Workshop VIII (Ottawa) and in the 10th Asia-Pacific Productivity Conference (Brisbane). The constructive comments of three referees are gratefully acknowledged.

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Constructions of economic functions and calculations of marginal rates in DEA using parametric optimization methods

Cited by 65 publications

References 12 publications

Marginal Values and Returns to Scale for Nonparametric Production Frontiers

Marginal Values and Returns to Scale for Nonparametric Production Frontiers

Terminal units in DEA: definition and determination

Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration

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