金融发展对经济增长影响的实证分析

本文将金融发展作为一个独立解释变量引入,构造经济增长的面板数据模型,运用面板数据以1994年为分界点分两阶段实证分析了全国及东、中、西部地区1985~2003年金融发展对经济增长的影响,以及东、中、西部地区影响的差异性,模型较好地拟合了数据。实证分析表明,各地区之间金融发展的不平衡性可以部分解释其经济增长的差异性。     

基于级差最大化组合赋权TOPSIS灰关联投影法的区域碳效率动态评价

针对传统碳效率评价多为单一经济产出的静态评价的不足,从经济、福利及人口等角度构建碳效率综合评价指标体系;通过把TOPSIS、灰色关联理论和矢量投影法引入到碳效率评价中,在传统加法合成和乘法合成组合赋权的基础上利用级差最大化法对评价指标赋权;并进一步引入时间变量对时间序列赋权,利用时序算术平均算子对前后两次加权进行集成,提出一种基于级差最大化组合赋权的TOPSIS灰关联投影动态评价模型。以泛长三角区域为例进行实证研究显示:该区域碳效率水平存在明显差异,产业发展不均衡。研究认为,应充分挖掘该区域的战略优势和发展潜力,协同发展,实现低碳经济。     

基于SVR的RAR区域经济预测模型

由于区域经济系统中许多经济变量呈现出强非线性与大波动性的特征,使得传统的时间序列线性建模和预测技术难以适应区域经济预测的要求.为此,提出基于支持向量机改进的残差自回归区域经济预测模型.首先采用时间序列分析中的残差自回归模型对时间序列趋势进行线性拟合,然后对残差自回归模型估计后的残差序列采用支持向量回归方法再次提取其非线性特征,从而提高区域经济时间序列模型的预测精度.最后以广东省GDP的预测实例说明模型的有效性.     

基于模型的小域估计方法及其拓展——由单变量推广到多变量情形

小域估计是当今抽样调查领域的研究热点,通过建立小域估计模型,将所有调查区域通过公共的参数连接起来,利用相关区域数据作为辅助信息,解决多层次抽样调查中某些域样本量不足的问题,提高参数的估计精度。本文首先在目标变量是单变量的情形下,研究了域层次模型和单元层次模型的参数估计方法,进而推广到目标变量是多变量的情形,考虑变量之间的相关性,研究了多变量Fay-Herriot模型参数的经验最优线性无偏预测及其均方误差矩阵的性质,最后通过一个实例,验证了参数估计具有良好效果。     

基于时变逆风参数的适应性金融市场动力学模型

建立了进化的适应性金融市场模型,通过引入时变的逆风参数,将其作为内生变量,以及进化的适应性测度来说明在一个三类投资者的市场中,有了逆风者的参与,在一定范围内,通过调整逆风者在图表分析者中所占的比例,可以使得市场趋于稳定,活跃市场;同时讨论了选择强度以及做市商敏感度两类参数对稳定性区域的影响.     

一类基于属性变量的DEA模型

系统评价不仅应重视系统的经济特征,还必须重视系统的非经济特征.在评价系统的非经济特征时必须考虑属性变量,即不能以货币衡量的反映系统非经济特征的定量变量.在过去关于DEA的研究中并未涉及到这种变量.本文对属性变量进行了明确的定义,并提出一种基于属性变量的DEA效率评价模型,最后通过实例演示了模型的合理性.     

区域物流能力与区域经济发展耦合互动机理研究

采用虚拟变量法和耦合协调度模型,利用内蒙古相关统计数据,探索了区域物流能力与区域经济发展之间的耦合互动关系.结果表明:区域物流能力对区域经济发展具有显著的正向影响,区域经济发展是区域物流能力的格兰杰原因,区域物流能力与区域经济发展之间存在耦合互动效应;不同耦合协调度等级下,区域物流能力对区域经济发展的影响程度存在较大差异.最后,基于以上研究结论提出相关政策建议,实现区域物流能力与区域经济协调发展.     

带有组结构的稀疏模型的参数估计和变量选择方法

本文针对带有组结构的广义线性稀疏模型,引入布雷格曼散度作为一般性的损失函数,进行参数估计和变量选择,使得该方法不局限于特定模型或特定的损失函数.本文比较研究了Ridge,SACD,Lasso,自适应Lasso,组Lasso,分层Lasso,自适应分层Lasso和稀疏组Lasso共8种惩罚函数的特点和引入模型后参数估计和...     

基于部门背景-计划行为理论改进模型的公务人员节能减排行为意图影响因素研究——以山西省为例

本文采用改进的传统计划行为理论模型,增加了知识-态度-行为以及利社会行为的理论基础作为变量,首次引入部门背景作为重要变量,旨在研究公务人员的节能减排行为意图的影响因素。本文开发出山西省政府部门公务人员节能减排行为意图问卷,建立了山西省政府部门公务人员节能减排行为意图模型,发现了不同部门对公务人员节能减排行为意图的影响程度和方向。结果显示模型整体契合度良好,不同部门的政府部门公务人员在知识变量、价值变量、知觉行为控制变量、行为意图变量上有显著差异。在利社会行为、主观规范变量和态度变量上没有显著差异。发改委公务人员主观规范较高,交通运输厅公务人员对未来展现节能减排行为的意愿与可能性较低。建设厅执行节能减排的后果有较低的评价。     

模型集成误差与优化研究

将模型作为函数,模型的输入输出参数为变量,给出变量真值的定义。在此基础上,给出单一模型误差e,利用误差e求出模型输出变量真值的期望μ,用μ作为修正值以提高计算结果的精确度。通过给出模型集成误差λ的定义,详细推导了λ的计算方法,利用λ计算模型集成输出变量真值y的期望μy、方差σy2、σy2/vy2,μy作为最终计算结果的修正值,而σy2/vy2可作为模型集成选择的依据,对同一输出变量而言,σy2/vy2小的模型集成是较优的．     

区域经济发展智能预测方法

分析了影响区域经济发展的各种因素,指出由于这些因素相互制约、相互影响,使得传统的经济预测方法越来越难以胜任区域经济发展预测的需要.论述了核方法在处理非线性、不确定性和不精确性数据上存在的优势,建立了基于核方法三种经济预测模型,并将这三种预测模型与其它两种预测方法一起,对区域经济的发展进行组合预测.最后,采用数据融合的方法将各个体模型的预测结果进行集成,作为最终的输出.实际的结果表明,基于核方法的组合预测技术能取得较为理想的预测效果.     

Potential inventory cost reductions using advanced time series forecasting techniques

This paper compares demand forecasts computed using the time series forecasting techniques of vector autoregression (VAR) and Bayesian VAR (BVAR) with forecasts computed using exponential smoothing and seasonal decomposition. These forecasts for three demand data series were used to determine three inventory management policies for each time series. The inventory costs associated with each of these policies were used as a further basis for comparison of the forecasting techniques. The results show that the BVAR technique, which uses mixed estimation, is particularly useful in reducing inventory costs in cases where the limited historical data offer little useful information for forecasting. The BVAR technique was effective in improving forecast accuracy and reducing inventory costs in two of the three cases tested. In the third case, unrestricted VAR and exponential smoothing produced the lowest experimental forecast errors and computed inventory costs. Furthermore, this research illustrates that improvements in demand forecasting can provide better cost reductions than relying on stochastic inventory models to provide cost reductions.     

我国宏观经济非参数联立模型的局部线性工具变量变窗宽估计

联立方程模型在经济政策制定、经济结构分析和预测方面起重要作用，目前关于非参数计量经济模型的研究主要停留在单方程模型上，而联立方程模型的研究在国际上刚刚起步，本将非参数回归模型的局部线性估计方法与传统联立方程模型估计方法相结合，首次提出了非参数计量经济联立模型的局部线性工具变量变窗宽估计并应用于我国宏观经济非参数联立模型，结果表明：我国宏观经济非参数联立模型优于线性联立模型且线性模型将造成不必要的人为设定误差；对于非参数联立模型，局部线性工具变量变窗宽估计优于局部线性估计。     

Validation problems and technological change in an input‐output model

The problems of validation of an input‐output model for forecasting the effects of clothing imports on the textile and other industries in the Australian economy are described. These relate to incomplete data requiring estimators to complete a fundamental matrix and allowance for technological change from one previous time period to another. Although reasonable reconciliation was achieved between computed results and industry observations and data over the most recent historical periods for which data is available, the use of the method for forecasting requires allowance to be made for the inclusion of forecasted technological changes via endogenous variables or other means. The method described for the Australian study throws light on some important economic issues which required qualification during the period used for validation. It is operated on a desk top computer.     

正权重组合预测模型及其在经济中的应用

介绍了组合预测的方法,并利用最优组合和递归方差倒数方法对组合预测方法进行改进;提出通过GMDH方法首先对影响经济预测模型的各变量进行筛选然后再建立回归模型、神经网络模型等单项预测模型的思想;最后结合GMDH方法建立的时间序列模型,建立正权重组合预测模型．     

Modeling mortality with a Bayesian vector autoregression

Parametric mortality models capture the cross section of mortality rates. These models fit the older ages better, because of the more complex cross section of mortality at younger and middle ages. Dynamic parametric mortality models fit a time series to the parameters, such as a Vector-auto-regression (VAR), in order to capture trends and uncertainty in mortality improvements. We consider the full age range using the Heligman and Pollard (1980) model, a cross-sectional mortality model with parameters that capture specific features of different age ranges. We make the Heligman–Pollard model dynamic using a Bayesian Vector Autoregressive (BVAR) model for the parameters and compare with more commonly used VAR models. We fit the models using Australian data, a country with similar mortality experience to many developed countries. We show how the Bayesian Vector Autoregressive (BVAR) models improve forecast accuracy compared to VAR models and quantify parameter risk which is shown to be significant.     

Energy policy modelling in developing and industrializing countries

A large number of models have been developed and used for energy policy planning, in a regional, national or international level, in order to cope with the broad variety of issues related to the energy problem. Energy models belong to the class of policy models, which address fuzzy and complex issues involving many non-quantitative factors, such as political issues, behavioural aspects, etc., as well as many uncertainties and lack of rigorous knowledge concerning the structure of the reference system, and the interrelationships of its elements. The role of energy policy models is very important, since they enhance understanding and communication, and they assist the policymakers to review plausible future configurations of relevant decision variables and parameters. In this paper one of the most important areas of energy modelling is investigated, that concerning the interactions between energy and economy in the group of Developing and Industrializing Countries (DIC's). It is pointed out that energy models used in the DIC's must capture the particular features of energy policy in these countries, such as rapid economic development fueled by expensive, depleting and often imported energy resources; dependence on foreign resources such as energy, capital, technology, etc.; management of indigenous resources, social structural changes, rapid population growth, urbanization and industrialization. In order to improve energy models and enhance their contributions in policy analysis, it is proposed that modelling efforts should be directed towards a better understanding of the energy-economy relationships in the DIC's, as well as towards the development of validated data bases.     

基于修正的时间序列模型对冲击负荷的预测

电力负荷预测的实质是对电力市场需求的预测,是利用以往的历史数据资料找出电力负荷的变化规律,进而预测负荷在未来时期的变化趋势.由于经济、气候以及工业生产等诸多因素的约束和限制,电力负荷预测精度很难提高.一个好的实用的电力负荷预测模型则要求既能充分利用负荷的历史数据,又能灵活方便地综合考虑其他多种相关因素的影响.提出了回归与自回归模型相结合的时间序列混合回归预测模型,它的待估参数由BP神经网络进行修正,经实例验证,预测效果良好.     

A comparison of univariate methods for forecasting container throughput volumes

In this paper, six univariate forecasting models for the container throughput volumes in Taiwan’s three major ports are presented. The six univariate models include the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey model, the hybrid grey model, and the SARIMA model. The purpose of this paper is to search for a model that can provide the most accurate prediction of container throughput. By applying monthly data to these models and comparing the prediction results based on mean absolute error, mean absolute percent error and root mean squared error, we find that in general the classical decomposition model appears to be the best model for forecasting container throughput with seasonal variations. The result of this study may be helpful for predicting the short-term variation in demand for the container throughput of other international ports.     

Padé Approximation in Economics

In economics, the systematic treatment of data to obtain specific properties from long (or short) data series is a main objective. The use of rational models and related numerical methods can be useful to help to predict the behaviour of relevant economic variables with a certain degree of certainty. This paper is concerned with illustrating the application of several numerical methods, in particular, the corner method, epsilon-algorithm, rs-algorithm and qd-algorithm, to the problem of model identification in time series analysis. These methods, closely related to theoretical research in Padé Approximation, are proposed to identify some type of rational structure associated with economic data in different contexts (financial, marketing, farming). Moreover, they incorporate the expectations of exogenous economic variables to improve the fit and forecasting of classic time series models.