基于DEA和SFA的我国商业银行效率研究

本文利用板块数据,分别采用非参数前沿法中的DEA法和参数前沿法中的SFA法对我国十四家商业银行1997-2001期间的综合效率进行了测度,在此基础上对两种方法测度出的银行效率值排序进行了相关分析和一致性检验,结果表明两种方法测度出的银行效率在数值上有显著差异,但在是效率排序上具有很好的一致性。     

基于PCA-DEA和PCA-SFA的大型综合医院绩效评价

分别采用组合PCA-DEA和组合PCA-SFA两种新方法对湖南省35家大型综合医院的综合效率进行测度,在此基础上对两种方法测度出的医院效率值及其排序进行了相关性分析和一致性检验,结果表明两种方法测度出的医院的综合效率在数值上有显著的差异,但是在效率排序上具有很好的一致性.     

基于改进DEA模型的科技投入产出有效性分析

合理地评价各地区的科技投入产出,对于资源的合理利用,提高资金的使用效率具有十分重要的现实意义.采用一种改进的DEA模型(简称M DEA),对2000—2002年间我国各地区的科技投入产出相对效率进行了充分评价和排序.并将三年的数据放在一起组成一个新的参考集,用同样的M DEA模型进行评估,得出各地区三年科技投入产出相对效率的变化情况.     

基于一致性排序的动态评价方法研究

主要工作是通过Copealand重排序方法构建动态评价模型,研究动态评价中的一致性排序问题,并通过实证研究验证模型的有效性.首先,利用基尼系数赋权法建立评价模型,确定静态评价结果和排名;然后,通过Copealand法确定出一个综合不同年份排序的一致性排序;最后,根据评价对象不同年份排名的发展趋势对一致性排序进行修正,得到最终的排序.主要特色一是通过Copealand法集合不同年份的排序确定最终的一致性排序,避免了通过引入时间权向量确定动态权重时人为主观因素太强的问题,解决了动态综合评价中的一致性排序问题;二是通过对一致性排序的修正,保证了最终的排序能够体现不同年份的发展趋势.     

我国商业银行效率测度及其影响因素分析

本文首先采用DEA方法对1999-2004年我国14家商业银行的技术效率、纯技术效率、规模效率进行了测度,在此基础上利用Panel Data模型对影响我国银行效率的若干因素进行了检验,结果表明自有资本比率和资产费用率对银行效率有显著影响,贷款质量、资产市场份额与银行效率值之间呈现较弱的相关关系,产权结构多元化有利于提高银行效率.     

一种新的带有参数的DEA有效单元排序模型

本文对数据包络分析中的有效单元排序方法进行了研究, 从一个新 的角度,定义最优有效和最劣无效, 提出了一种带有参数的有效单元排序模型.本文还给出并证明了此模型的一些性质, 并与其他排序模型进行了比较, 证明了本文模型的优越性. 最后用一个实例, 检验了此模型的可行性.     

中部六省科技资源配置效率研究——基于CCR-BCC模型的分析

科技资源配置效率主要由科技投入和产出决定.选取2012-2015年的主要投入和产出指标数据,建立C~2R-BC~2静态效率评价模型和Malmquist动态效率评价模型进行效率分析.根据计算出的结果给出相关建议:提高中部六省的科技资源配置效率,应培育科技创新的思想意识;创造适宜的科技创新环境;引进培养高级专业技术人才集聚科技创新队伍;加大科技资本投入力度尤其是加大对基础研究的投资.     

基于乘性一致的区间数互补判断矩阵排序法

首先通过分析乘性一致模糊互补判断矩阵的定义,给出了衡量判断矩阵一致性程度的新指标,在此基础上,结合区间数互补判断矩阵一致性和满意一致性定义,建立了判断矩阵完全一致和满意一致两种情况下的二次规划模型,通过求解得出判断矩阵的区间权重向量,最后提出了一种新的可能度公式对方案进行排序和择优.通过算例说明了此方法的可行性和简洁性.     

一种基于散度的模糊数排序方法

引用一种距离测度及模糊数的权重面积,建立了一种基于散度的模糊数排序指标.新的排序指标不仅引入了两个参考对象,即两个模糊数的极大和极小(M),(N),同时还考虑了模糊数本身的影响和决策者的决策态度.排序方法不仅计算简单、易于操作,而且还具有良好的性质.算例分析表明本文所提出的排序方法在一定程度上克服了现有方法的缺陷.     

数据包络分析SBM超效率模型无可行解问题的两阶段求解法

数据包络分析是一种得到广泛应用的基于线性规划的非参数技术效率分析方法,其超效率模型是将被评价DMU从参照集中排除从而使求解得出的效率值可能大于1.超效率模型在文献中用于对有效的DMU进行排序、探测异常值、敏感性和稳定性分析、分析生产率变化的Malmquist模型、二人博弈模型等.超效率模型存在的一个缺陷是在规模收益可变的假设下会出现无可行解的问题.提出了一种基于两阶段求解的SBM超效率模型,并保持了与传统SBM超效率模型的兼容性:在传统的投入(产出)导向SBM超效率模型有可行解时,两阶段法获得的结果与之相同;在传统的投入(产出)导向SBM超效率模型无可行解时,两阶段超效率模型可以得出最接近投入(产出)导向定义的可行解.算例采用实际数据对方法进行了验证.     

Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA

This study surveys the increasing research field of performance measurement by making use of a bibliometric literature analysis. We concentrate on two approaches, namely Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) as the most important methods to evaluate the efficiency of individual and organizational performance. It is the first literature survey that analyses DEA and SFA publications jointly, covering contributions published in journals, indexed by the Web of Science database from 1978 to 2012. Our aim is to identify seminal papers, playing a major role in DEA and SFA development and to determine areas of adoption. We recognized a constant growth of publications during the years identifying DEA as a standard technique in Operations Research, whereas SFA is mainly adopted in Economic research fields. Making use of document co-citation analysis we identify Airports and Supplier Selection (DEA) as well as Banking and Agriculture (SFA) as most influential application areas. Furthermore, Sensitivity and Fuzzy Set Theory (DEA) as well as Bayesian Analysis and Heterogeneity (SFA) are found to be most influential research areas and seem to be methodological trends. By developing an adoption rate of knowledge we identify that research, in terms of citations, is more focusing on relatively old and recent research at the expenses of middle-aged contributions, which is a typical phenomenon of a fast developing discipline.     

A comparison of chance-constrained DEA and stochastic frontier analysis: bank efficiency in Taiwan

We employed both chance-constrained data envelopment analysis (CCDEA) and stochastic frontier analysis (SFA) to measure the technical efficiency of 39 banks in Taiwan. Estimated results show that there are significant differences in efficiency scores between chance-constrained DEA and stochastic frontier production function. The advanced setting of the chance-constrained mechanism of DEA does not change the instinctive differences between DEA and SFA approaches. We further find that the ownership variable is still a significant variable to explain the technical efficiency in Taiwan, irrespective of whether a DEA, CCDEA or SFA approach is used.     

基于SMAA和公共权重的决策单元效率评价与排序

在数据包络分析(DEA)中,公共权重模型是决策单元效率评价与排序的常用方法之一。与传统DEA模型相比,公共权重模型用一组公共的投入产出权重评价所有决策单元,评价结果往往更具有区分度且更为客观。本文考虑决策单元对排序位置的满意程度,提出了基于最大化最小满意度和最大化平均满意度两类新的公共权重模型。首先,基于随机多准则可接受度分析(SMAA)方法,计算出每个决策单元处于各个排名位置的可接受度;然后,通过逆权重空间分析,分别求得使最小满意度和平均满意度最大化的一组公共权重;最后,利用所求的公共权重,计算各决策单元的效率值及相应的排序。算例分析验证了本文提出的基于SMAA的公共权重模型用于决策单元效率评价与排序的可行性。     

基于AHP和DEA联用的城市交通环境可持续发展评价方法研究

将层次分析法(AHP)和数据包络分析(DEA)相结合构建了两种方法的联用评价模式,对使用该方法评价城市交通环境可持续发展水平和后续决策指导方面进行了探讨.该方法对AHP法在多个决策单元定量对比分析和DEA体现决策者偏好方面进行了改善.从影响城市交通环境可持续发展水平的城市综合发展、道路设施水平、交通运输功能和交通环境质量等方面出发,建立了城市交通环境可持续发展评价体系,并应用于深圳市实证分析,通过AHP分析认为深圳市2000～2007年城市交通环境可持续发展水平在波动中缓慢上升,再通过27个城市2005年数据DEA有效性分析和投影分析,得到深圳市在城市交通环境可持续发展方面的总体效率为0.9729,并得到了其要达到DEA有效的调整方案和部分政策建议.     

Comparing frontier methods for economic–environmental trade-off analysis

This paper uses a mechanistic frontier approach as a reference to evaluate the ability of conventional parametric (SFA) and non-parametric (DEA) frontier approaches for analyzing economic–environmental trade-offs. Conventional frontier approaches are environmentally adjusted through incorporating the materials balance principle. The analysis is worked out for the Flemish pig finishing case, which is both representative and didactic. Results show that, on average, SFA and DEA yield adequate economic–environmental trade-offs. Both methods are good estimators for technical efficiency. Cost allocative and environmental allocative efficiency scores are less robust, due to the well-known methodological advantages and disadvantages of SFA and DEA. For particular firms, SFA, DEA and the mechanistic approach may yield different economic–environmental trade-offs. One has therefore to be careful when using conventional frontier approaches for firm-specific decision support. The mechanistic approach allows for optimizing performances per average present finisher, which is the production unit in pig finishing. Conventional frontier methods do not allow for this optimization since the number of average present finishers varies along the production functions. Since the mechanistic production function is based on underlying growth, feed uptake and mortality functions, additional firm-specific indicators can also be calculated at each point of the production function.     

Cost efficiency and scale economies in general dental practices in the US: a non-parametric and parametric analysis of Colorado data

This paper uses both the non-parametric method of data envelopment analysis (DEA) and the econometric method of stochastic frontier analysis (SFA) to study the production technology and cost efficiency of the US dental care industry using practice level data. The American Dental Association 2006 survey data for a number of general dental practices in the state of Colorado in the US are used for the empirical analysis. The findings suggest that the cost efficiency score is between 0.79 and 0.87, on average, and the cost inefficiency is mostly due to allocative rather than technical inefficiency. The optimal output level for a dental practice to fully exploit the economies of scale is estimated to be at $1.68 million. Average cost at this level of output is 50.6 cents for each dollar of gross billing generated. The DEA and SFA approaches provide generally consistent results.     

有关Pareto解在DEA中的应用的探讨

对文[1]中有关DEA有效(C2R)的定理4,本文给出了在某种条件下的逆定理,以便简化DEA有效性(C2R)的判断.     

Network DEA pitfalls: Divisional efficiency and frontier projection under general network structures

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there are two types of network DEA models. One is developed under the standard multiplier DEA models based upon the DEA ratio efficiency, and the other under the envelopment DEA models based upon production possibility sets. While the multiplier and envelopment DEA models are dual models and equivalent under the standard DEA, such is not necessarily true for the two types of network DEA models. Pitfalls in network DEA are discussed with respect to the determination of divisional efficiency, frontier type, and projections. We point out that the envelopment-based network DEA model should be used for determining the frontier projection for inefficient DMUs while the multiplier-based network DEA model should be used for determining the divisional efficiency. Finally, we demonstrate that under general network structures, the multiplier and envelopment network DEA models are two different approaches. The divisional efficiency obtained from the multiplier network DEA model can be infeasible in the envelopment network DEA model. This indicates that these two types of network DEA models use different concepts of efficiency. We further demonstrate that the envelopment model’s divisional efficiency may actually be the overall efficiency.