多年时滞教育经济投入占用产出模型与教育结构研究

提出一个新的考虑多年时滞的动态投入占用产出模型研究教育结构,采用阶变法处理投入产出模型的非线性系数,编制2000年教育经济投入占用产出表,计算教育规模和人力资本需求和教育经费投入,发现高级人力资本在总量上,特别是结构上的短缺是中国经济发展的一个严重问题.     

资本占用系数与直接消耗系数变动

生产能力利用程度的变化会引起资本占用系数的变化。本文从边际投入的角度,研究了资本占用系数变化对投入产出表中直接消耗系数的总体影响,并对我国1987—1990年投入产出表进行计算,得出在这两年直接消耗系数的总体变动主要是由资本占用系数的变动所致。     

占用变动的SDA模型及其应用

结构分解分析(StructuralDecompositionAnalysis,SDA)模型是近年来投入产出分析领域主流经济分析工具,研究了考虑占用变动的SDA模型一般方法,并利用1992年、1997年中国乡镇企业与非乡镇企业炼焦工业投入占用产出表,测算了影响中国乡镇炼焦工业就业和固定资本占用变动的各因素大小.     

考虑资本闲置的多年时滞人力资本投入占用产出模型

人力资本理论强调的是由教育所新增的人力资本对经济增长的决定作用.为了规划与中国经济持续增长相适应的人力资本需求,建立了一个新颖的考虑资本闲置的多年时滞投入占用产出模型.为了考虑人力资本时滞长的特点以及人力资本占用的损耗补偿,在斯通人口投入产出模型和Leontief动态投入产出模型基础上,提出了多年时滞人力资本投入占用产出模型.将LDS模型推广为考虑资本闲置的多年时滞人力资本投入占用产出模型,以便考虑影响教育产出的外生因素,并推导出既适于产出增加也适于产出减少的正解.应用上,预测至2015年的各种教育水平人力资本的总量和部门需求,发现高级人力资本短缺和人才行业结构不合理是经济发展需要解决的重要问题.     

考虑资本补偿的动态投入占用产出分析基本模型

本文依据投入占用产出分析的基本思想,建立了动态投入占用产出分析的基本模型,该模型有效地解决了动态投入产出分析中的资本补偿问题。     

投入占用产出框架下Leontief逆矩阵的变动分析

Leontief逆矩阵是投入产出分析的核心概念。研究直接消耗系数的扰动对Leontief逆矩阵的影响是投入产出分析中结构变动问题的重要议题。本文研究了在投入占用产出框架下,各部门的固定资产消耗对Leontief逆矩阵的变动影响。     

投入占用产出表中纯部门占用额及占用系数的计算方法问题

本文给出根据一定的假设,用产业部门占用矩阵及产出矩阵,推算纯部门的占用额及直接占用系数的方法。并给出完全占用系数的计算方法。     

投资内生化的房地产投入占用产出局部闭模型及其应用

房地产在国民经济中占据特殊地位,为全面反映房地产与国民经济间的相互联系和促进关系,设计和构造了房地产投入占用产出表.分析表中数据可知,房地产发展对国民经济的主要影响与其他生产部门不同,主要通过增加企业毛盈余和政府收入进而拉动投资增长,最终实现经济增长.鉴于传统投入产出模型不能对此进行有效刻画,提出新的将投资内生化的投入产出局部闭模型.应用模型测算房地产对经济的拉动效应,得出的测算结果与传统模型相比明显偏大.     

网络团购供应链委托代理激励机制研究

运用委托代理理论的基本原理和方法,以一个供应商和一个团购网站组成的二级供应链为基础,建立了供应商为委托人、团购网站为代理人的博弈模型,研究了供应商对团购网站的激励机制,并利用最优化原理进行了求解得出了最优激励强度系数,并给出了算例.结果表明:总代理成本随风险规避度、市场随机因素方差和产出系数的增加而增加,随团购网站成本系数的增加而减少;供应商确定性等价收益和供应商支付给团购网站的报酬随团购网站成本系数、风险规避度和市场随机因素方差的增加而减少,随团购网站产出系数的增加而增加.     

基于主成分-SFA的银行投入产出效率评价模型及实证

通过测算贷款、存款等投入要素对净利息收入的贡献,评价商业银行的投入产出效率,对银行的资本运营和监管机构的银行资本监管具有重要意义.原始投入变量过多和变量之间的高度相关都会对评价模型的估计和检验产生影响.创新和特色在于:一是通过提取互不相关的2个主成分,反映6个原始投入变量95%以上的信息.建立基于主成分的SFA模型,克服变量过多和变量高度相关对模型参数估计和检验的影响,解决原始投入变量高度相关导致的系数检验不显著和符号不正确问题.二是利用主成分回归,将主成分与投入变量的关系表达式代入基于主成分的SFA模型,进而确定投入变量的权重系数,建立银行的投入产出模型,反映6个投入变量对净利息收入的影响规律.实证研究结果表明:一是利用主成分建立的SFA模型系数检验显著,技术效率随时间增加.二是利息支出、贷款余额、总资产、存款总额、固定资产和员工人数产出弹性分别为0.287,0.272,0.254,0.086,0.072和0.053.因此影响银行净利息收入的主要因素为利息支出、贷款余额、总资产.存款总额、固定资产和员工人数对净利息收入的影响较小.三是18家商业银行的规模系数为1.025,银行的净利息收入表现出规模经济特征.     

WTO条件下国家经济规划波动区间分析

本讨论了在WTO条件下外部因素对国家经济的影响,并从动态投入产出模型最优解的角度,运用凸集理论和线性规划理论探讨了汇率变动对国家整体经济影响,最后给出了动态投入产出模型最优解变动范围的估计式。     

基于MSM输入输出排队交换结构的调度算法研究

信息网络的传输宽带化和业务多样化需求给交换结构的可扩展性及性能提出了更高的要求.基于MSM(Memory-Space-Memory)输入输出排队交换结构进行研究,通过对交换网络内部拥塞调度机制进行深入分析,在保障调度公平性的基础上,提出一种严格匹配输出调度过程的SM-RR(Strict Matching algorithm based onRound-Robin)算法.与现有典型算法相比,该算法无需加速比即可获得较高的性能.     

主成分分析法在产业关联度研究中的应用

在产业经济学中，影响力系数和感应度系数是常用的评价产业部门拉动作用和推动作用的方法。本文针对影响力系数和感应度系数的缺陷，采用主成分分析法对投入产出表中的完全消耗系数进行研究，分析产业关联度，以确定国民经济主导产业。最后以中国1997年投入产出表为例进行了分析。结果表明，本方法更加符合实际，具有重要的参考价值。     

Monotonicity-preserving bootstrapped Kriging metamodels for expensive simulations

Kriging metamodels (also called Gaussian process or spatial correlation models) approximate the Input/Output functions implied by the underlying simulation models. Such metamodels serve sensitivity analysis, especially for computationally expensive simulations. In practice, simulation analysts often know that this Input/Output function is monotonic. To obtain a Kriging metamodel that preserves this characteristic, this article uses distribution-free bootstrapping assuming each input combination is simulated several times to obtain more reliable averaged outputs. Nevertheless, these averages still show sampling variation, so the Kriging metamodel does not need to be an exact interpolator; bootstrapping gives a noninterpolating Kriging metamodel. Bootstrapping may use standard Kriging software. The method is illustrated through the popular M/M/1 model with either the mean or the 90% quantile as output; these outputs are monotonic functions of the traffic rate. The empirical results demonstrate that monotonicity-preserving bootstrapped Kriging gives higher probability of covering the true outputs, without lengthening the confidence interval.     

Multi-product production planning: A goal programming approach

Cost minimization multi-product production problems with static production resource usage and internal product flow requirements have been solved by linear programming (LP) with input/output analysis. If the problem is complicated by interval resource estimates, interval linear programming (ILP) can be used. The solution of realistic problems by the above method is cumbersome. This paper suggests that linear goal programming (LGP) can be used to model a multi-product production system. LGP's unique modeling capabilities are used to solve a production planning problem with variable resource parameters. Input/output analysis is used to determine the technological coefficients for the goal constraints and is also used to derive an information sub-model that is used to reduce the number of variable resource goal constraints. Preliminary findings suggest that the LGP approach is more cost-efficient (in terms of CPU time) and in addition provides valuable information for aggregate planning.     

福建省科技投入强度的预测预警研究

本文在分析福建省历年R＆D强度及特征的基础上．构建了R＆D强度的灰色预测模型和增长型曲线外推模型,并利用它们构建了组合预测模型,同时以全国平均水平为依据构建了预警系统,得到2008—2010年的R＆D强度预测值分别为1．0321％、1．1186％、1．2115％,预警结果3年都是巨警。结果表明,福建省的R＆D强度在2010年达不到1．7％的水平,这与福建省“十一五”R＆D投入强度的既定目标相去甚远。随后,文章提出了福建省实现“十一五”科技投入强度目标的条件要求,为政府制订和调整科技投入政策提供参考。     

A novel order diminution of LTI systems using Big Bang Big Crunch optimization and Routh Approximation

A novel computer aided method is presented that yields Linear Time Invariant Reduced Order System (LTI-ROS). This is accomplished by using the combined benefits of Big Bang Big Crunch (BBBC) optimization and Routh Approximation (RA) method. The applicability of the proposed method for both Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) Linear Time Invariant (LTI) systems is verified by solving numerical examples. Furthermore, the results show that the realized LTI-ROS retain stability and steady state conditions of original system. The reduced order system also exhibits lower Integral Square Error (ISE) values.     

Identification of dynamic systems using Multiple Input–Single Output (MISO) models

System identification is a method used to obtain the modal characteristics of existing structural systems through dynamic observations. Modal characteristics of the system can be used for a variety of purposes, including model updates, damage assessment, active control and original design re-evaluation. In this paper, the transfer functions relating the input quantities (traffic load, wind speed and temperature variations) and output quantities (lateral and longitudinal movement) of the towers of the Bosphorus Suspension Bridge were defined with the help of two models, namely, the parametric Multiple Input–Single Output (MISO) Auto-Regressive with eXogenous input (ARX) and the multiple regression models. The latter model was primarily used to check for the existence of outlier measurement(s) and to identify the input quantities that have a significant contribution to the structural movements since outlier measurements in observations and insignificant input quantities increases the difficulty of defining the parameters of the inherently complex MISO ARX model. Least Squares (LS) and bi-square weighted robust predictors were used to determine the parameters of the multiple regression model used in this study. Regression analysis showed that there were no outlier measurements in the tower observations and the effect of wind speed on the longitudinal movements was statistically insignificant. Furthermore, the sensitivity of LS and bi-square robust predictors to outlier measurements were also checked in the regression analysis by adding rough errors to the observations. Finally, it was also observed that the MISO ARX512, ARX511, ARX411 and ARX415 models defined by taking into account the results of regression analysis estimate structural movements more accurately than the multiple regression model ARX010.     

Linear programming system identification: The general nonnegative parameters case

By linear programming system identification, we mean the problem of estimating the objective function coefficient vector π and the technological coefficient matrix A for a linear programming system that best explains a set of input–output vectors. Input vectors are regarded as available resources. Output vectors are compared to imputed optimal ones by a decisional efficiency measure and a likelihood function is constructed. In an earlier paper, we obtained results for a simplified version of the problem. In this paper, we propose a genetic algorithm approach for the general case in which π and A are of arbitrary finite dimensions and have nonnegative components. A method based on Householder transformations and Monte Carlo integration is used as an alternative to combinatorial algorithms for the extreme points and volumes of certain required convex polyhedral sets. The method exhibits excellent face validity for a published test data set in data envelopment analysis.     

Methodology for determining the acceptability of system designs in uncertain environments

In practice, managers often wish to ascertain that a particular engineering design of a production system meets their requirements. The future environment of this design is likely to differ from the environment assumed during the design. Therefore it is crucial to find out which variations in that environment may make this design unacceptable (unfeasible). This article proposes a methodology for estimating which uncertain environmental parameters are important (so managers can become pro-active) and which combinations of parameter values (scenarios) make the design unacceptable. The proposed methodology combines simulation, bootstrapping, design of experiments, and linear regression metamodeling. This methodology is illustrated through a simulated manufacturing system, including fourteen uncertain parameters of the input distributions for the various arrival and service times. These parameters are investigated through the simulation of sixteen scenarios, selected through a two-level fractional–factorial statistical design. The resulting simulation Input/Output (I/O) data are analyzed through a first-order polynomial metamodel and bootstrapping. A second experiment with other scenarios gives some outputs that turn out to be unacceptable. In general, polynomials fitted to the simulation’s I/O data can estimate the border line (frontier) between acceptable and unacceptable environments.