基于DEA的我国上市银行X效率分析

本文使用非参数的数据包络分析(DEA)来测度13家上市商业银行2010年度的X效率。研究结果显示:11家上市商业银行技术效率(TE)达到DEA有效,其他银行技术无效的原因主要是规模效率(SE)无效;7家上市商业银行X效率达到DEA有效,其他银行X效率无效的原因主要是配置效率(AE)无效。本文在效率测度的基础上对非DEA有效的商业银行提出了改进意见,并提出我国上市商业银行提高X效率的对策。     

中国上市银行效率的实证研究——基于DEA三阶段模型分析

采用最新的DEA三阶段模型,测算了1996～2008年11家上市银行的效率。研究结果表明:去除环境因素和随机影响,银行的效率值与原始的DEA所测结果存在显著差异。文中对这种差异作了相应解释,并在此基础上提出提高我国上市银行效率的措施。     

银行效率研究中的指标选择

本文通过对银行效率研究文献的回顾,提出了基于DEA的银行效率分步式投入产出指标体系。     

我国商业银行并购效率的研究方法述评

文章首先阐述了商业银行并购效率研究的必要性和重要意义，在探讨技术效率、纯技术效率和规模效率等概念范畴的基础上论述了银行效率的内涵和改善路径，重点研究了银行并购效率的研究方法，其中包括参数分析与非参数分析方法，分析了非参数DEA方法的应用优势，介绍了可用于银行效率分析的CCR模型和BCC模型．     

加入WTO后我国商业银行业的效率变化

本文采用基于投入的DEA方法,选取1999-2002年我国14家资产规模较大的商业银行为样本计算出商业银行的技术效率,并将其分解为纯技术效率和规模效率,分析了加入WTO以后我国银行业的效率变化,并比较分析了国有商业银行与股份制商业银行效率变化以及二者之间的差距。我们发现,加入WTO之后,我国商业银行业的整体效率有了较大提高,导致商业银行技术非效率的主要因素是规模非效率;国有商业银行与股份制商业银行之间的效率差距并没有减少,二者的主要差距在于纯技术非效率。     

陕西烟草种植技术效率分析

本文采用SFA(随机前沿分析)及DEA(数据包络分析)方法分析了陕西烟草种植的技术效率,并对两种方法的分析结果进行了对比。分析结果显示DEA-VRS模型(可变规模报酬DEA)与SFA模型分析结果之间有很强的一致性。分析结果显示陕西烟草种植效率不高,仅为55%。在现有技术水平下,通过合理利用现有资源,可以将陕西烟草种植技术效率提高45%。     

估计前沿成本函数的一种优化方法

本文提出一种建立前沿成本函数表达式的优化模型,它将数据包络分析(DEA)与非线性优化方法结合起来估计成本函数的参数.DEA方法用来确定代表技术与配置有效性的管理偏差,然后采用优化方法来处理利用DEA方法得到的样本点以估计成本函数的参数.最后本文测算了AT＆T的前沿成本函数并利用它对AT＆T自然垄断的特征进行了分析,同时也与一些类似的研究结果进行了比较,以说明方法的合理性.     

基于DEA的企业技术创新效率评价研究——以安徽省16市工业企业为例

首先阐述了数据包络分析(DEA)方法的基本原理;然后建立基于投入和产出指标的安徽省技术创新效率评价指标体系,利用数据包络分析(DEA)的经典模型BC2测算2015年安徽省16个市技术创新的技术效率、纯技术效率和规模效率值;最后,根据安徽省16市技术创新效率评价情况,分析结果并提出建议.     

绿色信贷对商业银行效率的影响分析 ——基于16家上市银行的实证分析

习近平总书记在党的十九大报告中指出要加快生态文明体制改革,建设美丽中国,并明确提出要构建市场导向的绿色技术创新体系,发展绿色金融。绿色信贷的健康发展将直接推动绿色金融的进步和国家经济的可持续发展。本文首先运用DEA模型(数据包括分析法)对2010年—2017年16家上市银行的技术效率进行了测算,之后根据DEA评价结果,对样本数据的绿色信贷与银行效率进行了回归分析,最后对商业银行如何健康发展绿色信贷业务提出了政策性建议。     

我国商业银行效率与经营范围关系的实证研究——基于DEA模型与变系数模型的分析

本文选取了29家商业银行并依据其经营规模划分为全国性的和地方性的两类,运用数据包络模型分析法(DEA)对其在2005年至2010年的经营效率进行了测度,又运用了变系数模型对变量的显著性进行分析发现:2005年的地方性商业银行经营效率较高,而之后这两类银行效率并无显著性差异。分析认为,这与近年来的网络技术、科技创新等因素密不可分,并在最后对我国商业银行提高经营效率给出了相关的建议。     

政策支持、企业投资与旅游业效率

本文对30家旅游上市公司2009—2018年的数据包络分析效率值进行纠偏修正,并构建政策支持与企业投资对于旅游企业运营效率的影响因素模型,运用Bootstrap截断回归法进行实证分析。研究结果表明,旅游上市公司整体上处于非有效状态;政府补贴与金融支持会对企业运营效率产生抑制作用;企业投资对效率的提升作用是决定性的,远远大于政府补贴和金融支持的负面作用。     

Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers

In this paper we estimate the DEA technical efficiency for 4796 Brazilian municipalities, by applying a recently proposed “Jackstrap” method, which combines Bootstrap and Jackknife resampling techniques, to reduce the effect of outliers and possible errors in the data set. We perform calculations to identify and eliminate high leverage municipalities, using different variants of Data Envelopment Analysis (DEA), as well as Free Disposal Hull (FDH). Corroborating previous results, efficiency results for the Brazilian municipalities show a clear relationship between the size of the municipality and its efficiency scores. Indeed, under both DEA variants, smaller cities tend to be less efficient than larger ones hence indicating that the quality of the frontier adjustment improves significantly as the size of the municipality increases. We present arguments that may explain to some extent these findings, such as economies of scale and the excess spending due to revenue from royalties. However, such effects require further, more careful examination.     

DEA模型评价第三方物流企业服务项目绩效的应用分析

以物流成本投入与收益为输入和输出指标,通过运用DEA模型对我国某第三方物流企业的7个物流服务项目进行综合绩效评价与分析。找出企业在物流项目运作中的问题,对非DEA有效的投入和产出给出调整意见加以改进。     

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.     

Using DEA to evaluate the state of society as measured by multiple social indicators

This paper presents an approach for discussing the state of society, which is measured by multiple social indicators, using data envelopment analysis (DEA). Replacing inputs and outputs in DEA with negative and positive social indicators respectively, we analyze the desirability of living in the 47 prefectures of Japan. This is also a proposal for the potential use of DEA in multi-dimensional evaluation analysis other than the standard DEA efficiency analysis. The results using eight social indicators identify 26 DEA desirable prefectures out of the 47 and present other useful knowledge and information. It is concluded that DEA, which can avoid uniform evaluation by an a priori weighting system, provides availability as a comprehensive evaluation tool different from traditional ones.     

会计师事务所人力资源投入产出效率相对有效性分析

本文运用数据包络分析(DEA)中的CCR和BCC模型,对40家会计师事务所的人力资源投入产出效率的相对有效性进行评价,使他们看到自己与其他所的差距,从而更清楚地认识自己,分析它们为非DEA有效的原因,并根据DEA的投影定理提出相关的改进建议,为会计师事务所的经营决策提供帮助。     

Testing Independence in Models of Productive Efficiency

Bootstrap methods for inference in nonparametric models of productive efficiency can be simplified, reducing computational burden, when the independence condition implicitly assumed in Simar and Wilson (1998) holds. This paper surveys nonparametric tests of independence that might be useful in this context.     

The effect of innovation efficiency management on performance: Differences according to organizational size

The efficiency associated with innovation has been a frequently considered element in the literature. However, the conceptualization of this efficiency and its management is an underexplored factor. We study the way in which the different elements that conform to this efficiency are managed over time. From a dynamic efficiency analysis using data envelope analysis (DEA) and the Malmquist productivity index, we evaluate changes in efficiency and if there are differences according to firm size. Our results confirm the relevance of size in the way that firms manage their innovation efficiency and how small firms differ from larger ones in terms of efficiency management.     

Incorporating quality into the measurement of hospital efficiency: a double DEA approach

This paper uses “double DEA” to assess how accounting for quality influences DEA technical efficiency scores of a sample of 1,074 US hospitals. In the first use of DEA, quality indices are estimated using a variety of process and outcome measures of quality. In the second use of DEA, technical efficiency is assessed while controlling for quality. A variety of DEA quality indices and a DEA variety of efficiency models are compared to determine how the treatment of quality influences findings regarding technical efficiency. Controlling for efficiency does matter, with outcome measures having an apparently greater impact than process measures. Given the call for improved quality and better cost containment, controlling for quality is an important contribution to efficiency analysis.