基于最近距离投影的DEA两阶段效率评价方法研究

数据包络分析是进行效率评价最重要的方法之一。传统的数据包络分析理论主要寻找有效前沿面上的最远距离投影，在极大化无效性指数的同时也面临着效率改进的巨大难度和高额成本。对于具有两阶段内部生产结构的决策单元，本文从考虑最小改进难度的视角出发，提出了最近距离投影的两阶段效率评价方法。该方法首先得到所有强有效决策单元的线性组合，且这些组合均占优于被评价的两阶段决策单元。然后建立了两阶段范围调整效率评价模型，在确定具有最近投影距离的占优组合的同时，得到了两阶段评价效率。最后，本文运用我国32家上市银行的年度数据对所提出方法进行了应用验证。     

部分中间产出作为最终产品的两阶段合作效率

网络DEA模型研究多个进程和多个子系统的复杂系统的效率度量.针对中间产出无法为第2阶段完全消耗的情形,传统DEA模型对系统效率的评价偏高,已有网络DEA模型则对系统效率的评价偏低.本文提出部分中间产出作为最终产品,进入流通渠道的两阶段效率模型,用来测度两阶段生产过程的真实效率.采用乘积形式描述两阶段合作的特征,给出不同...     

基于两阶段交叉效率模型的中国商业银行效率评价

为了更好的评价我国商业银行的效率,在传统两阶段DEA模型中引入交叉效率,并对两阶段生产系统的内部结构进行优化,构建了考虑共享投入和非期望产出的两阶段交叉效率模型。最后结合中国16家商业银行2006-2015年的年度报表,分别应用传统的两阶段DEA模型和新模型评价样本银行的效率。结果表明：新模型比传统模型更客观、区分力更强,具有更强的稳定性,更适合用于评价我国商业银行的效率。     

具有双重角色和非期望要素的供应商评价两阶段DEA模型

在基于DEA方法的供应商评价过程中,会出现供应商同时具有双重角色要素和非期望输出要素的情形。文章提出了一个处理双重角色要素的新思路,认为DEA模型中双重角色要素同时扮演了输入和输出两个角色,所有双重角色要素本质上就是网络DEA模型中的中间变量,并同时认为包含双重角色要素的生产系统一定能分解成为多个子系统,其中的双重角色要素既是一个子系统的输入,同时又是另一个子系统的输出。为测度供应商的效率,基于已有关系型两阶段DEA模型,将供应商的生产运营视为两阶段过程,其双重角色要素视为两阶段过程的中间变量,并通过将所有输出以加权和的形式表达,但对非期望输出用负的权重的方式,提出了一个具有双重角色和非期望输出要素的供应商评价两阶段DEA模型,最后用一个实例验证了方法的可行。     

基于DEA的两阶段系统中间产品公平设定研究

在由两个子决策单元串联组成的两阶段系统中,公平设定中间产品目标对确保和激励两阶段相互合作以达到整个系统的最佳性能至关重要.数据包络分析(DEA)作为系统绩效评估的一种非参数方法吸引了众多学者的注意;基于此方法,本文提出一个考虑公平关切的两阶段DEA模型用于设定两阶段系统中间产品目标,并证明据此模型获得的设定方案正是一个纳什讨价还价博弈的均衡解.     

资源约束型两阶段生产系统的DEA效率评价模型

经典的数据包络分析(DEA)模型将决策单元看作"黑箱",忽视决策单元的内部过程,必然会高估决策单元的效率。本文研究了一种资源约束型两阶段生产系统的DEA效率评价方法,针对此类生产过程的内部过程,研究其内部运行机制对整体效率的影响。本文提出的模型实质上是一类特殊的网络DEA模型,其评价原理有别于已有的研究成果,但更有助于管理者确定生产过程的非有效来源及其效率改进方向。实例证实本文方法的合理性。     

基于完全包络面的DEA Super效率评价方法

为了对决策单元进行公平、合理、完全的排序,提出一种考虑完全包络面的DEA Super效率评价方法.讨论包络面的选取与DEA效率值的关系,提出完全包络面的概念,并在等产出图上演示了最优前沿面、最劣前沿面与完全包络面的区别和关系;提出基于最劣前沿面的Super效率DEA模型,给出基于完全包络面的DEA Super效率评价排序模型,目标函数包含决策单元的最优效率和最劣效率两部分.该方法用于决策单元的排序,同时考虑最符合与最不符合决策单元自身偏好的权重体系;该方法用于决策单元的效率排序,能获得比较合理的排序结果,可以实际应用于各种决策单元排序中.     

两阶段生产系统的DEA效率评价模型

数据包络分析(DEA)作为一种数学规划方法,已经被广泛用来评价一个决策单元相对于其它决策单元的效率。经典的DEA模型把决策单元看作一个“黑箱”,对决策单元的内部运行机制不作深入的研究。本文以一个两阶段生产系统为例,从生产系统的内部过程出发,提出一个基于DEA的模型以合理评估该决策单元的相对效率。本文提出的模型实质上是一类特殊的网络DEA模型,其评价原理有别于已有的研究成果,但确实有助于管理者确定生产过程(如供应链)的非有效来源及其效率改进方向。     

存在非期望输入输出的多阶段系统效率评价模型

现有多阶段DEA模型的研究普遍假设所有输入、输出均为期望指标, 对存在非期望指标的情况研究较少, 尚无对最初投入和中间产出带有非期望指标的研究。为此, 本文首先提出了输入、输出类型的判定方法, 并将其应用于存在非期望输入、输出的两阶段系统。进一步针对存在非期望指标的两阶段生产系统中同种输入、输出, 尤其中间产出类型判定一致与不一致的情况, 构建了相应的生产可能集以及两阶段DEA模型, 最后利用本文方法和模型对我国上市银行效率进行了评价。     

考虑环境属性约束的平行结构DEA模型

针对平行结构DEA模型在构造虚拟参考单元时没有考虑任何约束可能存在低估效率的情况,通过加入决策单元的环境属性对现有的平行结构模型进行了改进,从而在组合为虚拟参考单元的子决策单元之间添加必要的约束,使得模型所得的效率值更具现实意义。最后,将改进后的平行结构模型应用于中国各省份污染治理效率评价问题,从实证角度验证了模型的合理性和有效性。     

Two-stage DEA models with undesirable input-intermediate-outputs

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). Many studies have examined DEA efficiencies of two-stage systems, where all the outputs from the first stage are the only inputs to the second stage. Although single-stage DEA models with undesirable input-outputs have been extensively studied, there still lacks of more systematical investigation on two-stage DEA with undesirable variables. For instance, depending on its operating model, even whether an intermediate variable is desirable or undesirable can be questionable for a particular two-stage system. Furthermore, most of the existing studies on two-stage systems focus on the case where only the final outputs are undesirable. In this work, we try to systematically examine two-stage DEA models with undesirable input-intermediate-outputs. Particularly, we utilize the free-disposal axioms to construct the production possibility sets (PPS) and the corresponding DEA models with undesirable variables. The proposed models are then used to illustrate some theoretical perspectives by using the data of China׳s listed banks.     

金融监管对商业银行产品创新影响——基于两阶段DEA模型的研究

以金融监管为视角,从银监会政策发布到各商业银行设计理财产品产生收益,其政策效应过程是有衔接分阶段性的。本文运用两阶段的数据包络分析(Data Envelopment Analysis, DEA)方法,将监管环境下银行理财产品的运营过程分为创新产品设计与创新产品市场表现两个阶段,得到了2004年至2014年间我国银行业的创新效率,结果显示第一阶段的银行业产品创新效率和第二阶段的理财产品市场表现效率基本上呈现出正相关的关系,且金融监管对商业银行产品创新的影响效果有一定的滞后性。此外,通过计算出历年银行业创新效率投入指标的改进值,找出了效率非有效的年份与最佳效率的年份在投入产出上的差距,并从监管者的角度和银行操作层面为针对银行产品创新的监管改进提供了政策建议。     

Evaluation of bank branch growth potential using data envelopment analysis

Banks occasionally employ frontier efficiency analyses to objectively identify best practices within their organizations. Amongst such methods, Data Envelopment Analysis (DEA) was found to be one of the leading approaches. DEA has been successfully applied in many bank branch performance evaluations using traditional intermediation, profitability and production approaches. However, there has been little focus on assessing the growth potential of individual branches.This research presents five models that examine three perspectives of branch growth. Each model was applied to the branch network of one of Canada׳s top five banks to gauge the growth potential of individual branches and to provide tailored improvement recommendations. Using various analysis methodologies, the results of each model were examined and their functionality assessed.     

随机环境下的多投入多产出生产前沿面估计

传统的DEA模型假设观测样本的投入产出都是确定型数据, 这使得DEA在实际应用中受到限制, 本文提出的基于拟似然估计的多投入多产出随机非参数包络数据 (PLE-StoNED) 方法拓展了这个假设, 能够估计随机环境下的生产前沿面.本文证明, 生产可能集假设条件下的前沿面可以用一个有凹凸性和单调性限制的函数来表示.相较之前的StoNED方法, 本文提出的方法可以估计随机环境下多投入多产出决策单元 (DMU) 的前沿面.通过Monte Carlo实验, 多投入多产出PLE-StoNED方法的有效性得到验证, 它可纠正DEA等传统方法产生的偏误.最后, 实证研究部分运用这一新提出的方法估计了中国大陆商业银行的生产前沿面和效率.本文提出的方法弥补了DEA缺乏统计性的不足, 可为决策者在随机环境下对多投入多产出决策单元进行生产力和效率评估提供决策参考.     

Stage efficiency evaluation in a two-stage network data envelopment analysis model with weight priority

Conventional DEA models treat the entire production system as a black box and ignore its internal structures. To address this issue, many studies have examined the DEA efficiencies of two-stage systems in which all outputs of the first stage are the only inputs to the second stage. Based on game theory, the non-cooperative model and centralized model were developed for such a two-stage network structure. However, for the centralized model with multiple optimal solutions and the non-cooperative model, an assumption is required as to whether the first or second stage should be assigned the absolute priority for optimization. In many cases, certain circumstances might exist in which one stage does not completely dominate the other stage. In this paper, we develop a methodology for assessing the overall and stage efficiencies by considering the different and DMU-specific degree of priority given to the stages. Particularly, the non-cooperative model and the centralized model can be deemed as special cases. Moreover, we compare the proposed approaches with the existing approaches, which indicates that our approaches can greatly reduce the computational burden. Two empirical examples are used to demonstrate the proposed approach.     

Target intermediate products setting in a two-stage system with fairness concern

In a two-stage system with two divisions connected in series, fairly setting the target outputs for the first stage or equivalently the target inputs for the second stage is critical, in order to ensure that the two stages have incentives to collaborate with each other to achieve the best performance of the whole system. Data envelopment analysis (DEA) as a non-parametric approach for efficiency evaluation of multi-input, multi-output systems has drawn a lot of attention. Recently, many two-stage DEA models were developed for studying the internal structures of two-stage systems. However, there was no work studying fair setting of the target intermediate products (or intermediate measures) although unreasonable setting will result in unfairness to the two stages because setting higher (fewer) intermediate measures means that the first (second) stage must make more efforts to achieve the overall production plan. In this paper, a new DEA model taking account of fairness in the setting of the intermediate products is proposed, where the fairness is interpreted based on Nash bargaining game model, in which the two stages negotiate their target efficiencies in the two-stage system based on their individual efficiencies. This approach is illustrated by an empirical application to insurance companies.     

How to better identify the true managerial performance: State of the art using DEA

Our motivation is to detail a potential improvement on the three-stage analysis published by Fried et al. [Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 2002;17:157–74] that can distinguish true performers from those that may be advantaged by favourable environments or measurement errors. The method starts with data envelopment analysis (DEA), and continues with stochastic frontier analysis to explain the variation in organisational performance in terms of the operating environment, statistical noise and managerial efficiency. It concludes with DEA again using adjusted data to reveal a measure of performance based on management efficiency only. Our proposed contributions include (i) a comprehensive approach where total input and output slacks are identified simultaneously for non-radial inefficiencies before levelling the playing field, (ii) identifying percent adjustments attributable to the environment and statistical noise, and (iii) using a fully units-invariant DEA model.