转型期中国工业全要素生产率与效率——基于细分行业的随机前沿模型分析

本文利用1996-2007年我国工业36个细分行业的面板数据,通过建立随机前沿模型,测算了转型期我国工业及其细分行业的全要素生产率,以及其分解要素的波动情况。实证研究表明,1996-2007年我国TFP平均为4.76%,并呈逐步上升趋势;TFP提高的主要原因是技术进步,平均来说,TFP增长的85.50%来源于技术进步的贡献;因为技术效率的提高使TFP平均每年提高了0.94个百分点;资源配置效率的提高对TFP的贡献年平均0.52个百分点,并有较大的波动;而因为规模总报酬递减的影响,TFP每年反而下降了0.78个百分点。通过经济增长来源分析,我国工业经济增长由主要靠资本驱动逐渐向靠技术创新驱动转变,转型期经济增长方式已表现为集约型。     

中国粮食生产技术效率的实证研究——随机前沿面生产函数的应用

本文使用我国1992、1995年和1999年三年的县(市)级数据,采用超越对数随机前沿面生产函数,研究了我国粮食生产技术效率的空间分布和影响因素。发现:(1)技术效率在地理空间上的分布是不均衡的,呈现出南高北低的格局:(2)从92年到99年,全国平均技术效率大约下降1%,且效率分布的变异性似乎有加大的趋势;(3)农业生产的自然条件和一些社会经济因素对粮食生产的技术效率有着显著的影响。     

中国区域技术效率的随机前沿分析

本文采用1996-2006年全国30个省实际GDP、人力资本和物质资本的面板数据,在随机误差项自相关和异方差的假定下,运用随机前沿分析模型对我国省际技术效率进行测算;同时分东部、中部和西部进行分析,并进行了严格的假设检验。结果表明:我国中部地区的技术效率最高,东部次之,西部最低;且三大地区技术效率均逐年提高,三者之间的技术效率差异呈现收敛趋势。     

中国经济随机前沿模型效率的贝叶斯统计推断

基于扩展的随机生产前沿模型,研究了区域生产效率的差异和其影响因素的作用效果,应用贝叶斯统计方法对中国各省份2010-2017的年度数据(不包含港澳台地区,下同)进行了实证研究.研究发现:生产效率总体呈逐渐下降的趋势,地区间生产效率有一定的差异,高等教育规模对生产效率具有显著的直接影响.人力资本能有效促进东部和中部地区的经济增长,西部地区主要依靠资本促进经济增长.环境污染对中部地区的经济增长具有一定的负向作用.     

基于双前沿面数据包络分析的区间全局Meta-frontier Malmquist指数及其应用研究

针对传统DEA-Malmquist生产率指数建立在精确数据基础之上,缺乏对非精确数据的分析与研究,应用区间数来刻画投入产出数据的不确定性,同时考虑决策单元组间异质性、参考前沿面单一以及生产率不具可比性等问题,建立了基于双前沿面数据包络分析的区间全局Meta-frontier Malmquist指数,并提出了一种基于可能...     

基于SFA与DEA模型的创新型城市科技创新效率实证研究

分别运用SFA模型和DEA模型对10个样本城市2001-2010年的科技创新效率进行定量测度,并对两种方法测度出的创新型城市科技创新效率值排序进行相关分析和一致性检验.结果表明两种方法测度出的创新型城市科技创新效率在数值上有显著差异,但在效率排序上具有很好的一致性.     

基于空间滞后随机前沿模型技术效率的估计

为了刻画潜在的空间效应,本文提出了一类空间滞后随机前沿模型.在单边误差项分别服从半正态分布、指数分布及截尾正态分布的情况下,我们给出了模型的对数似然函数及迭代步骤.此外,应用JMLS方法给出了单边误差项的条件均值与条件众数,因而得到了相应的技术效率估计.     

考虑环境异质性的我国电网企业经营效率评价及影响因素研究

中国不同区域的电网企业在经营过程中面临着较为明显的环境异质性.文章将中国25家省级电网企业分为华北、华东、华中、东北、西北、西南区域6个群组,通过构建Meta-frontier DEA模型来测算2016-2019年25家省级电网的经营效率,对其无效率值进行分解,分析无效性来源,并进一步通过面板数据回归模型分析其主要影响因素.研究发现:1)观察期内,25家电网在共同前沿和群组前沿下的平均效率值分别为0.6900和0.9106,且共同前沿下的效率值均不超过群组前沿下的效率值;2)省级电网企业的技术差距比率(TGR)在不同区域有很大差异.西北、华东、华北地区的TGR高于其他的区域,说明这些区域的电网企业经营效率整体优于其它区域;3)根据无效率值分解结果,东北、华中和西南地区省级电网企业的效率损失更多来自于技术差距,华东和西北地区电网企业的效率损失中管理无效率占比更高,而华北地区省级电网企业则因技术差距和管理无效率同时造成一定的效率损失;4)城镇化率、地区GDP、地形及用户结构对该地区电网企业的技术差距无效率皆存在较为显著的影响,而区域的气候及用户密度对技术差距无效率的影响不显著.最后,根据研...     

技术进步效率测度模型的构建及应用

技术进步是影响经济增长的决定性因素,因此,技术进步的测度成为一个重要的研究问题。然而,技术进步的测度到目前为止还没有统一和令人信服的方法。本文使用DEA方法,基于理想生产前沿面构建了技术进步效率测度模型。技术进步效率测度模型是技术进步度量的一种新方法,是一个相对量的测量,规避了技术进步总量测算模型的一些缺陷。将模型用于测算我国1999～2008年的技术进步效率,测算结果表明我国大多数地区技术进步效率呈逐年上升的趋势,经济产出较高而同时消耗了较低资源投入的省份具有较高的技术进步效率。这说明低消耗的技术进步才是有效的,能够有效支撑经济的可持续发展。     

存在中间产品退出的混合型生产系统效率测度与分解研究

混合型生产系统是系统内部同时存在串联子系统和并联子系统的复杂生产系统.以存在中间产品退出的混合型生产系统为研究对象,针对其效率测度与分解问题在总系统效率函数表达、中间产品退出比例确定和子系统效率分解三方面的表征,构建了一种基于DEA理论框架的效率测度与分解模型.在求解该模型的过程中,借鉴交叉效率思想解决复杂模型非线性求解问题,并提出一种中立的第二目标规划优化效率分解中最优解不唯一问题.最后通过一个算例验证该模型的可行性和有效性.     

Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation

Isotonic nonparametric least squares (INLS) is a regression method for estimating a monotonic function by fitting a step function to data. In the literature of frontier estimation, the free disposal hull (FDH) method is similarly based on the minimal assumption of monotonicity. In this paper, we link these two separately developed nonparametric methods by showing that FDH is a sign-constrained variant of INLS. We also discuss the connections to related methods such as data envelopment analysis (DEA) and convex nonparametric least squares (CNLS). Further, we examine alternative ways of applying isotonic regression to frontier estimation, analogous to corrected and modified ordinary least squares (COLS/MOLS) methods known in the parametric stream of frontier literature. We find that INLS is a useful extension to the toolbox of frontier estimation both in the deterministic and stochastic settings. In the absence of noise, the corrected INLS (CINLS) has a higher discriminating power than FDH. In the case of noisy data, we propose to apply the method of non-convex stochastic envelopment of data (non-convex StoNED), which disentangles inefficiency from noise based on the skewness of the INLS residuals. The proposed methods are illustrated by means of simulated examples.     

Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis

Cross efficiency method is an extension of data envelopment analysis (DEA), and has been widely used for ranking performance of decision making units (DMUs). To eliminate the non-uniqueness of cross efficiency scores, the aggressive and benevolent strategies have been proposed as secondary goals to determine the unique cross efficiency score. The current paper aims to propose an alternative strategy which does not consider the preference of the decision maker in choosing aggressive or benevolent strategy. Instead, the paper considers all possible weight sets in weight space when computing the cross efficiency and each DMU is given an interval cross efficiency. By using the stochastic multicriteria acceptability analysis (SMAA-2) method, all DMUs in the interval cross efficiency matrix (CEM) could be fully ranked according to the acceptability indices. A numerical example about efficiency evaluation to seven academic departments in a university is illustrated.     

某些决策单元的EDA有效性的简便判别法

本给出了判别某些决策单元EDA有效性（C^2R或C^2GS^2）的简便方法。     

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。     

Equivalent solutions to additive two-stage network data envelopment analysis

In the additive approach of two-stage network data envelopment analysis (DEA), the non-linear DEA model is transformed into a parametric linear model and then solved by computing a series of linear programs. Lim and Zhu (2013; Integrated data envelopment analysis: Global vs. local optimum.European Journal of Operational Research, 229(1), 276–278) and Ang and Chen (2016; Pitfalls of decomposition weights in the additive multi-stage DEA model. Omega, 58, 139–153) propose two parametric linear approaches to solve additive two-stage network DEA model. The current study shows that the two approaches are equivalent and use the same parameter in searching for the global optimal solution.     

Achievement and benchmarking of countries at the Summer Olympics using cross efficiency evaluation method

In this paper, an extension tool of data envelopment analysis (DEA), namely cross efficiency evaluation method, is used to measure the performance of the nations participating in the last six Summer Olympic Games. The model in the paper considers two inputs (GDP per capita and population) and three outputs (number of gold, silver and bronze medals won), and the weight restrictions are included to guarantee that a unit of silver medal corresponds to a higher valuation than a unit of bronze medal, and the highest for gold medal. The results for the last six Summer Olympic Games are analyzed, and a unique ordering of the participants based on average cross efficiency is provided, also cluster analysis technique is used to select the more appropriate targets for poorly performing countries to use as benchmarks.     

Improving weak efficiency frontiers in the fuzzy data envelopment analysis models

Evaluating the performance of activities or organization by common data envelopment analysis models requires crisp input/output data. However, the precise inputs and outputs of production processes cannot be always measured. Thus, the data envelopment analysis measurement containing fuzzy data, called “fuzzy data envelopment analysis”, has played an important role in the evaluation of efficiencies of real applications. This paper focuses on the fuzzy CCR model and proposes a new method for determining the lower bounds of fuzzy inputs and outputs. This improves the weak efficiency frontiers of the corresponding production possibility set. Also a numerical example illustrates the capability of the proposed method.     

技术有效的决策单元与DEA有效性(C~2GS~2)

对李光金、阎洪先生所定义的技术有效的决策单元,证明了它是DEA有效(C~2GS~2)的,而且讨论了将非有效的决策单元转变为有效.     

The interval efficiency based on the optimistic and pessimistic points of view

Data envelopment analysis (DEA) is a data-oriented approach for evaluating the performances of a set of peer entities called decision-making units (DMUs), whose performance is determined based on multiple measures. The traditional DEA, which is based on the concept of efficiency frontier (output frontier), determines the best efficiency score that can be assigned to each DMU. Based on these scores, DMUs are classified into DEA-efficient (optimistic efficient) or DEA-non-efficient (optimistic non-efficient) units, and the DEA-efficient DMUs determine the efficiency frontier. There is a comparable approach which uses the concept of inefficiency frontier (input frontier) for determining the worst relative efficiency score that can be assigned to each DMU. DMUs on the inefficiency frontier are specified as DEA-inefficient or pessimistic inefficient, and those that do not lie on the inefficient frontier, are declared to be DEA-non-inefficient or pessimistic non-inefficient. In this paper, we argue that both relative efficiencies should be considered simultaneously, and any approach that considers only one of them will be biased. For measuring the overall performance of the DMUs, we propose to integrate both efficiencies in the form of an interval, and we call the proposed DEA models for efficiency measurement the bounded DEA models. In this way, the efficiency interval provides the decision maker with all the possible values of efficiency, which reflect various perspectives. A numerical example is presented to illustrate the application of the proposed DEA models.