经济活动中环境费用的投入产出分析

本文通过对传统投入产出表进行环境费用外部化，区分了经济活动中各种环境损害费用和保护费用，并在此基础上，讨论了国民经济各部门总产出中的直接和完全的环境保护使用量的确定问题．最后，以1982年天津市投入产出表为例进行了实例研究．     

一类变系数动态投入产出模型

本文从国民经济各部门所采用新技术的比例出发,提出了一类新型的变系数动态投入产出模型,讨论了离散型动态模型的三种解法(正向递推法、反向递推法和综合推导法)和此模型下的动态逆,以及连续型动态模型的两种特殊形式.     

中国数字经济增长影响因素的结构分解分析

基于投入产出模型,运用结构分解(SDA)技术测算了2012-2017年我国最终需求变动、进口变动及技术进步对中国数字经济部门总产出增长的影响.研究显示:1)国内最终需求是数字经济部门总产出增长的主要动力;2)进一步分解结果显示,技术进步中,数字技术进步是数字经济部门总产出增长的关键因素;国内最终需求中,需求水平是数字经济部门总产出增长的关键因素;3)数字经济制造业与数字经济服务业的增长机制具有异质性,数字经济制造业主要依靠最终需求水平的拉动,数字经济服务业还受到最终需求结构的变动及技术进步的影响.     

产业关联分析动态网络模型

投入产出关联效应分析模型应用完全经济关联关系的概念,较为全面的反映了产业部门间的关联关系.然而,传统的投入产出关联效应分析对产业部门间的关联关系做了简化处理,并没有考虑经济活动的传导时滞因素所产生的动态影响,这种处理方式使模型的应用受到较多限制.利用有向加权网络工具,考虑经济活动的传导时滞,构建了产业关联分析动态网络模型.该模型与传统投入产出模型相比具有以下三个特点:一是考虑了产业部门间及部门内部传导时滞的作用,对部门间关联效应及传导过程作了动态描述,使其关联关系更加符合实际;二是可计算任意时点上一个部门产量的变化对系统中各部门关联效应的大小;三是该模型经济含义直观,易于理解,且易于利用计算机仿真模拟.     

中国部门生产链演化趋势及动态影响分析

研究部门间生产链关系及其演化趋势和生产链上、下游顺次动态影响情况。利用中国1987～2005年不变价序列投入产出表和APL模型,计算发现1987～2005年国民经济中存在四条重要的生产链,分析了这一时期四条生产链的演化趋势。进一步,以“黑色金属矿开采业－黑色金属冶炼及压延加工业－金属制品业”生产链为例,基于向量自回归模型,运用脉冲响应函数和方差分解分析了生产链上、下游部门相互间的动态影响情况。结果有助于确定国民经济总体战略及制定相关产业政策,并在制定经济调控政策时更好地预期政策的时效性。     

目标规划在离散型动态投入产出模型中的应用

以离散型动态投入产出模型为约束条件的主体、以决策部门所希望达到的种种目标为约束条件的附加部分,建立动态投入产出目标规划模型.通过求解动态投入产出目标规划模型而得到离散型动态投入产出模型的解.此解法与其它解法相比具有更大的实用价值.     

最终需求为时序变量的投入产出决策模型

针对最终需求为正态随机变量的投入产出模型,建立了一个时序模型来估计最终需求的期望与方差,并由此给出了总产出在一定置信度下的取值空间.这样就使得随机投入产出模型在决策时可具体操作.     

基于投入产出线性规划模型的燃料乙醇影子价格研究

影子价格能够反映资源的稀缺性及对国民经济的贡献程度.应用投入产出与线性规划相结合的分析方法,构建了投入产出线性规划模型,并基于笔者编制的2005年燃料乙醇投入产出表,针对燃料乙醇制造业部门的不同发展速度,运用投入产出线性规划模型对燃料乙醇的影子价格进行了具体测算.     

基于有向加权网络的价格传导时滞模型

传统的投入产出价格模型利用矩阵描述并分析价格传导过程.因矩阵表述方式属二维模型,故而无法反映价格随时间变化的动态关系.利用有向加权网络描述各产业部门及部门间的价格传导关系,并将时间维度引入构建了价格传导网络模型,考察价格传导时滞的影响.与现有投入产出价格模型相比,模型具有以下三个优势:一是可计算任意时刻的价格传导波动,而传统投入产出价格模型只是该模型在价格传导时间趋向无穷时的特例;二是模型考虑了各部门不同的价格传导时滞的影响,对价格传导过程描述更加精确;三是该模型不受二维矩阵算法的限制,计算复杂度低,易于仿真模拟.     

投入产出关联网络模型及其统计属性研究

从复杂网络的视角,建立了各产业部门之间的投入产出关联复杂网络模型,利用国民经济核算司发布的投入产出数据,分析了投入产出关联网络的边权分布、强度分布和聚集系数等主要网络属性,尝试揭示了我国国民经济系统中各产业部门之间复杂的投入产出关联关系.     

初中学生心理测量的统计分析

本文用相关分析说明了〈中学生素质特点分类培养〉项目研究中所用各种心理诊断量表整体组合运用的合理性 ;用因子分析简化了测试项目的指标体系 ,选定了适当的公共主因子 ,并对公因子给予了合理的解释 ;用聚类分析依据因子得分对学生实施心理素质特点分类 ;根据学生的心理素质特点类型提出了相应的宏观培养策略     

统计方法在研究微积分与后续课程相关性中的应用与实证分析

着眼于本科生后期专业课程的学习效果,探讨微积分课程学习效果的有效性.以北京理工大学某经管类专业全部学生大一大二两学年的学习成绩为依据,分析微积分课程学习与后续理科课程学习的相关性,提出评估微积分课程学习效果的量化指标θ值的概念,并基于回归分析和相关性分析给出其算法.在实例分析中,通过研究,发现学生微积分课程学习效果的好坏会影响他们对后续相关课程的学习,同时也发现学生将微积分知识运用到间接相关科目的能力比运用到直接相关的科目的能力薄弱.     

Enabling e-transactions with multi-attribute preference models

This paper describes potential applications of multi-attribute preference models (MAPM) in e-commerce and offers some guidelines for their implementation. MAPM are methodologies for modeling complex preferences that depend on more than one attribute or criterion, and include multi-attribute utility theory, conjoint analysis, and the Analytic Hierarchy Process. There are numerous examples of applications in e-commerce that would benefit from the acquisition of information regarding the preferences of a consumer, a customer, an advice seeker, or a decision maker. Here, the focus is on applications of MAPM models in B2C and B2B websites, where preferences of consumers are assessed for the purpose of identifying products or services that closely match their needs.     

What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions

Evaluating the economic attractiveness of large projects often requires the development of large and complex financial models. Model complexity can prevent management from obtaining crucial information, with the risk of a suboptimal exploitation of the modelling efforts. We propose a methodology based on the so-called “differential importance measure (D)$(D)$” to enhance the managerial insights obtained from financial models. We illustrate our methodology by applying it to a project finance case study. We show that the additivity property of D grants analysts and managers full flexibility in combining parameters into any group and at the desired aggregation level. We analyze investment criteria related to both the investors’s and lenders’ perspectives. Results indicate that exogenous factors affect investors (sponsors and lenders) in different ways, whether exogenous variables are considered individually or by groups.     

A sensitivity analysis algorithm for hierarchical decision models

In this paper, a comprehensive algorithm is developed to analyze the sensitivity of hierarchical decision models (HDM), including the analytic hierarchy process and its variants, to single and multiple changes in the local contribution matrices at any level of the decision hierarchy. The algorithm is applicable to all HDM that use an additive function to derive the overall contribution vector. It is independent of pairwise comparison scales, judgment quantification techniques and group opinion combining methods. The allowable range/region of perturbations, contribution tolerance, operating point sensitivity coefficient, total sensitivity coefficient and the most critical decision element at a certain level are identified in the HDM SA algorithm. An example is given to demonstrate the application of the algorithm and show that HDM SA can reveal information more significant and useful than simply knowing the rank order of the decision alternatives.     

我国工业污染分布状况研究

环境污染越来越受到我国的重视,而工业污染是造成环境污染的重要原因.为了对我国的环境污染分布状况进行深入了解,从总量出发,利用聚类分析和因子分析法分析了我国工业污染的分布情况,并探讨了各类地区工业污染差异的原因,为我国进行侧重点治理环境污染提出可行性建议.     

Composite multilinearity,epistemic uncertainty and risk achievement worth

Risk achievement worth is one of the most widely utilized importance measures. RAW is defined as the ratio of the risk metric value attained when a component has failed over the base case value of the risk metric. Traditionally, both the numerator and denominator are point estimates. Relevant literature has shown that inclusion of epistemic uncertainty (i) induces notable variability in the point estimate ranking and (ii) causes the expected value of the risk metric to differ from its nominal value. We investigate the conditions under which the equality of the nominal and expected values of a reliability risk metric holds. We then study how the presence of epistemic uncertainty affects RAW and the associated ranking. We propose an extension of RAW (called ERAW) which allows one to obtain a ranking robust to epistemic uncertainty. We discuss the properties of ERAW and the conditions under which it coincides with RAW. We apply our findings to a probabilistic risk assessment model developed for the safety analysis of NASA lunar space missions.     

When does data envelopment analysis outperform a naïve efficiency measurement model?

This paper compares the results from data envelopment analysis (DEA) to a naïve efficiency measurement model, which generates a scalar efficiency score by averaging all output–input ratios. Random data and real-life data are used to test the relative performance of the naïve model against various DEA models. The results suggest that the proposed the naïve model replicates DEA efficiency scores almost perfectly for constant return-to-scales and low heterogeneity in output–input data. It is therefore concluded that heterogeneity in output–input data is important to take advantage of the capability of DEA. It is also shown that heterogeneity is more relevant to efficiency measurement than the number of dimensions.     

Singular Value Decomposition and Its Visualization

Singular value decomposition (SVD) is a useful tool in functional data analysis (FDA). Compared to principal component analysis (PCA), SVD is more fundamental, because SVD simultaneously provides the PCAs in both row and column spaces. We compare SVD and PCA from the FDA view point, and extend the usual SVD to variations by considering different centerings. A generalized scree plot is proposed to select an appropriate centering in practice. Several useful matrix views of the SVD components are introduced to explore different features in data, including SVD surface plots, image plots, curve movies, and rotation movies. These methods visualize both column and row information of a two-way matrix simultaneously, relate the matrix to relevant curves, show local variations, and highlight interactions between columns and rows. Several toy examples are designed to compare the different variations of SVD, and real data examples are used to illustrate the usefulness of the visualization methods.