Measurement of Returns-to-Scale using Interval Data Envelopment Analysis Models

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe economic concept of Returns-to-Scale (RTS) has been intensively studied in the context of Data Envelopment Analysis (DEA). The conventional DEA models that are used for RTS classification require well-defined and accurate data whereas in reality observations gathered from production systems may be characterized by intervals. For instance, the heat losses of the combined production of heat and power (CHP) systems may be within a certain range, hinging on a wide variety of factors such as external temperature and real-time energy demand. Enriching the current literature independently tackling the two problems; interval data and RTS estimation; we develop an overarching evaluation process for estimating RTS of Decision Making Units (DMUs) in Imprecise DEA (IDEA) where the input and output data lie within bounded intervals. In the presence of interval data, we introduce six types of RTS involving increasing, decreasing, constant, non-increasing, non-decreasing and variable RTS. The situation for non-increasing (non-decreasing) RTS is then divided into two partitions; constant or decreasing (constant or increasing) RTS using sensitivity analysis. Additionally, the situation for variable RTS is split into three partitions consisting of constant, decreasing and increasing RTS using sensitivity analysis. Besides, we present the stability region of an observation while preserving its current RTS classification using the optimal values of a set of proposed DEA-based models. The applicability and efficacy of the developed approach is finally studied through two numerical examples and a case study

Sensitivity of DEA models to measurement errors

One of the weak points of DEA (Data Envelopment Analysis) models indicated in literature[1,2] is their sensitivity to variable measurement errors. The occurrence of data interference, whichis the basis of the productivity analysis, may distort the classification of the units and may causemisjudgement of their effectiveness.In the article the results of simulation concerning the DEA models sensitivity to occurrence andfeatures of random element in the monitored variables describing the model are presented.The set of thirty DMU (Decision Making Units) described by the means of three inputvariables, two output variables and one environmental variable was analysed. On the basis of thedetermined initial value of all the kinds of variables for each DMU, their relative effectiveness andtheir ranking were determined. Then, the value of each variable was interfered randomly with thenoise with normal distribution N(m,σ) and once again relative effectiveness and ranking of DMUwere determined. The calculation was done repeatedly, taking into account different levels ofvariance. The simulation carried out in the described manner was the basis for the assessment ofthe stability of the classification with the occurrence of measurement errors.On the basis of the research, the limits of DEA models resistance to the occurrence of errors inthe data that are used for productivity analysis were determined.In the authors’ opinion, the proposals in the article may be recognised as a vital input for thedevelopment of the methodology of comparative productivity analysis by the means of DEAmodels

A proxy approach to dealing with the infeasibility problem in super-efficiency data envelopment analysis

Super-efficiency data envelopment analysis (SE-DEA) models are expressions of the traditional DEA models featuring the exclusion of the unit under evaluation from the reference set. The SE-DEA models have been applied in various cases such as sensitivity and stability analysis, measurement of productivity changes，outliers’ identification，and classification and ranking of decision making units (DMUs). A major deficiency in the SE-DEA models is their infeasibility in determining super-efficiency scores for some efficient DMUs when variable, non-increasing and non-decreasing returns to scale (VRS, NIRS, NDRS) prevail. The scope of this study is the development of an oriented proxy approach for SE-DEA models in order to tackle the infeasibility problem. The proxy introduced to the SE-DEA models replaces the original infeasible DMU in the sample and guarantees a feasible optimal solution. The proxy approach yields the same scores as the traditional SE-DEA models to the feasible DMUs

A proxy approach to dealing with the infeasibility problem in super-efficiency data envelopment analysis

Super-efficiency data envelopment analysis (SE-DEA) models are expressions of the traditional DEA models featuring the exclusion of the unit under evaluation from the reference set. The SE-DEA models have been applied in various cases such as sensitivity and stability analysis, measurement of productivity changes，outliers’ identification，and classification and ranking of decision making units (DMUs). A major deficiency in the SE-DEA models is their infeasibility in determining super-efficiency scores for some efficient DMUs when variable, non-increasing and non-decreasing returns to scale (VRS, NIRS, NDRS) prevail. The scope of this study is the development of an oriented proxy approach for SE-DEA models in order to tackle the infeasibility problem. The proxy introduced to the SE-DEA models replaces the original infeasible DMU in the sample and guarantees a feasible optimal solution. The proxy approach yields the same scores as the traditional SE-DEA models to the feasible DMUs

Mechanisms of direct reactions with halo nuclei

Halo nuclei are exotic nuclei which exhibit a strongly clusterised structure: they can be seen as one or two valence nucleons loosely bound to a core. Being observed at the ridge of the valley of stability, halo nuclei are studied mostly through reactions. In this contribution the reaction models most commonly used to analyse experimental data are reviewed and compared to one another. A reaction observable built on the ratio of two angular distributions is then presented. This ratio enables removing most of the sensitivity to the reaction mechanism, which emphasises the effects of nuclear structure on the reaction.Comment: Invited talk given by Pierre Capel at the "10th International Conference on Clustering Aspects of Nuclear Structure and Dynamics" (Cluster12), Debrecen, Hungary, 24-28 September 2012. To appear in the Cluster12 Proceedings in the Open Access Journal of Physics: Conference Series (JPCS). (5 pages, 3 figures

Sensitivity analysis of network DEA illustrated in branch banking

Users of data envelopment analysis (DEA) often presume efficiency estimates to be robust. While traditional DEA has been exposed to various sensitivity studies, network DEA (NDEA) has so far escaped similar scrutiny. Thus, there is a need to investigate the sensitivity of NDEA, further compounded by the recent attention it has been receiving in literature. NDEA captures the underlying performance information found in a firm?s interacting divisions or sub-processes that would otherwise remain unknown. Furthermore, network efficiency estimates that account for divisional interactions are more representative of a dynamic business. Following various data perturbations overall findings indicate positive and significant rank correlations when new results are compared against baseline results - suggesting resilience. Key findings show that, (a) as in traditional DEA, greater sample size brings greater discrimination, (b) removing a relevant input improves discrimination, (c) introducing an extraneous input leads to a moderate loss of discrimination, (d) simultaneously adjusting data in opposite directions for inefficient versus efficient branches shows a mostly stable NDEA, (e) swapping divisional weights produces a substantial drop in discrimination, (f) stacking perturbations has the greatest impact on efficiency estimates with substantial loss of discrimination, and (g) layering suggests that the core inefficient cohort is resilient against omission of benchmark branches. Various managerial implications that follow from empirical findings are discussed in conclusions.

Recent developments in the eikonal description of the breakup of exotic nuclei

The study of exotic nuclear structures, such as halo nuclei, is usually performed through nuclear reactions. An accurate reaction model coupled to a realistic description of the projectile is needed to correctly interpret experimental data. In this contribution, we briefly summarise the assumptions made within the modelling of reactions involving halo nuclei. We describe briefly the Continuum-Discretised Coupled Channel method (CDCC) and the Dynamical Eikonal Approximation (DEA) in particular and present a comparison between them for the breakup of 15C on Pb at 68AMeV. We show the problem faced by the models based on the eikonal approximation at low energy and detail a correction that enables their extension down to lower beam energies. A new reaction observable is also presented. It consists of the ratio between angular distributions for two different processes, such as elastic scattering and breakup. This ratio is completely independent of the reaction mechanism and hence is more sensitive to the projectile structure than usual reaction observables, which makes it a very powerful tool to study exotic structures far from stability.Comment: Contribution to the proceedings of the XXI International School on Nuclear Physics and Applications & the International Symposium on Exotic Nuclei, dedicated to the 60th Anniversary of the JINR (Dubna) (Varna, Bulgaria, 6-12 September 2015), 7 pages, 4 figure

One-neutron halo structure by the ratio method

We present a new observable to study halo nuclei. This new observable is a particular ratio of angular distributions for elastic breakup and scattering. For one-neutron halo nuclei, it is shown to be independent of the reaction mechanism and to provide significant information about the structure of the projectile, including binding energy, partial-wave configuration, and radial wave function of the halo. This observable offers new capabilities for the study of nuclear structure far from stability.Comment: 9 pages, 4 figure

Quantitative selection of hedge funds using data envelopment analysis

Previous studies have documented that Data Envelopment Analysis(DEA) could be a good tool to evaluate fund performance,especially the performance of hedge funds as it can incorporatemultiple risk-return attributes characterizing hedge fund's nonnormal return distribution in an unique performance score. Thepurpose of this paper is to extend the use of DEA to the contextof hedge fund selection when investors must face multi-dimensionalconstraints, each one associated to a relative importance level.Unlike previous studies which used DEA in an empirical framework,this research puts emphasis on methodological issues. I showedthat DEA can be a good tailor-made decision-making tool to assistinvestors in selecting funds that correspond the most to theirfinancial, risk-aversion, diversification and investment horizonconstraints.hedge funds, data envelopment analysis, fund selection, performance measurement, alternative investment

Fiscal Decentralization and Public Sector Efficiency: Evidence from OECD Countries

This paper attempts to identify the effect of fiscal decentralization on public sector efficiency (PSE). We employ data envelopment analysis on a panel of 21 OECD countries over the period 1970-2000 to construct two alternative PSE indicators that reflect the governmental goals of economic performance and stability. In turn, using a novel technique that merges the methodologies of Simar and Wilson (2007) and Khan and Lewbel (2007), we regress the PSE scores obtained on an extensive set of alternative fiscal decentralization measures. Backed by strong empirical results, obtained from a number of different specifications, we contend that PSE is increasing with fiscal decentralization.public sector efficiency, fiscal decentralization, semi-parametric models