我国农村电气化建设与用电的发展趋势研究

本文首先介绍了我国农村电气化及用电的发展历程,结合发展现状及"十二五"农村电气化及用电的发展目标,分析了农村电气化建设及用电的发展趋势和特点。最后,针对新农村的电气化建设提出了相应建议。     

Extending the Lee–Carter model: a three-way decomposition

In this paper, we focus on a Multi-dimensional Data Analysis approach to the Lee–Carter (LC) model of mortality trends. In particular, we extend the bilinear LC model and specify a new model based on a three-way structure, which incorporates a further component in the decomposition of the log-mortality rates. A multi-way component analysis is performed using the Tucker3 model. The suggested methodology allows us to obtain combined estimates for the three modes: (1) time, (2) age groups and (3) different populations. From the results obtained by the Tucker3 decomposition, we can jointly compare, in both a numerical and graphical way, the relationships among all three modes and obtain a time-series component as a leading indicator of the mortality trend for a group of populations. Further, we carry out a correlation analysis of the estimated trends in order to assess the reliability of the results of the three-way decomposition. The model's goodness of fit is assessed using an analysis of the residuals. Finally, we discuss how the synthesised mortality index can be used to build concise projected life tables for a group of populations. An application which compares 10 European countries is used to illustrate the approach and provide a deeper insight into the model and its implementation.     

Under weighting of Private Information by Top Analysts

It is conventionally perceived in the literature that weak analysts are likely to under weight their private information and strategically bias their announcements in the direction of the public beliefs to avoid scenarios where their private information turns out to be wrong, whereas strong analysts tend to adopt an opposite strategy of over weighting their private information and shifting their announcements away from the public beliefs in an attempt to stand out from the crowd. Analyzing a reporting game between two financial analysts, who are compensated based on their relative forecast accuracy, we demonstrate that it could be the other way around. An investigation of the equilibrium in our game suggests that, contrary to the common perception, analysts who benefit from information advantage may strategically choose to understate their exclusive private information and bias their announcements toward the public beliefs, while exhibiting the opposite behavior of overstating their private information when they estimate that their peers are likely to be equally informed.     

Forecasting Connecticut home sales in a BVAR framework using coincident and leading indexes

We develop a Bayesian Vector Autoregressive Model (BVAR) to forecast home sales in Connecticut. In addition to home prices and mortgage interest rates, we also include measures of current and future economic conditions to see if these variables provide useful information with which to forecast Connecticut home sales. The best performing model incorporates recently developed coincident and leading employment indexes for Connecticut. These composite indexes perform markedly better than the inclusion of individual variables such as the unemployment rate or housing permits authorized.     

Big losses in ecosystem niches: how core firm decisions drive complementary product shakeouts

This study examines shakeouts in the context of business ecosystems. Market turbulence generated by core firm decisions in competing differentiated ecosystems can generate financial losses and exit for complementary niche market firms. I develop hypotheses predicting which niche markets will suffer larger losses and be more susceptible to shakeouts, and how core firm decisions will drive complementor performance and survival. I then apply these hypotheses to brand‐based differentiated ecosystems in the automotive industry, where networks of suppliers, customers, and complementors surround car manufacturers. More specifically, I study the complementary niche market of automotive leasing, where manufacturers sway leasing markets through product change, entry, and subsidization. To test the hypotheses, I use a proprietary dataset of 200,000 individual car leases between 1997–2002 to identify how manufacturer product design and niche market entry drive complementor losses and exit. These data allow a unique opportunity to understand how the strategic choices of core firms can have substantial and often devastating effects on niche markets in their ecosystem. Further, the results suggest how the dynamic capabilities to adapt to core firm behavior might improve performance for certain niche market complementors. Copyright © 2008 John Wiley & Sons, Ltd.     

基于季节时间序列模型的民航客运需求预测分析

随着经济的不断发展,人们的生活水平不断提升,消费理念也有很大改变,民用航空运输业也获得了飞速发展,越来越多的人选用航空这一交通方式出行。文章主要介绍了季节时间序列模型,并使用1998年1月至2013年5月的月度数据建立SARIMA模型,并对2013年的民航客运量进行预测分析。通过使用SARIMA模型对我国民航客运量进行预测,以期对民航企业制定合理的运营决策提供一些参考。     

基于Copula函数特征筛选的电力物资供应商投标价格预测

分析电力物资供应商投标价格特征是招标企业估计投标价格并制定合理采购策略的基础.传统的Spearmen秩相关系数特征筛选方法只单纯考虑了特征的数值排列顺序,不能挖掘特征变量与目标的内在关联关系,会导致预测效果差.本文在电力企业招标价格预测建模中引入Copula函数,通过特征变量与目标变量的联合概率分布来分析变量间的相依关系.首先确定特征变量与目标变量的边缘分布,进行Copula参数估计,选取合适的Copula函数并计算相关系数来筛选供应商投标价格的特征,并以多种预测方法进行预测来验证引入Copula函数后预测精度是否提升.结果表明,引入Copula函数进行特征筛选后,预测精度更高、效果更好.     

2010年和2020年全国煤炭供需预测

结合当前的经济发展态势,分别对2010年、2020年全国煤炭需求和供应进行了预测,并提出了相应的对策措施建议。预测2010年全国煤炭需求量为26.5亿～28.5亿t,比国家发展和改革委员会正式发布的《煤炭工业发展"十一五"规划》的26亿t多0.5亿～2.5亿t;预测2020年全国煤炭需求量为31亿～34亿t,比《煤炭工业中长期发展规划(国家发改委)》的28亿t多3亿～6亿t。预测未来15年我国需要新建和扩建煤矿规模达12亿～15亿t,其中"十一五"期间为4亿～6亿t。     

美国对华贸易救济的新趋势及其对策

美国对华贸易救济呈现出一些新趋势,其主要表现在:对华反倾销救济数量开始回落;与此同时,对华反规避、反补贴、特殊保障措施、知识产权调查等模式逐渐兴盛,并对中国对美出口产生相当大的影响。本文立足于这一现实,探讨新趋势产生的原因及其对中国的启示。     

Forecasting tourism revisited

This article examines the accuracy and efficiency of forecasting techniques by applying time series regression to forecasting visitor arrivals. Past studies have shown that simpler time series techniques perform as well or better than complex forecasting models. An assessment of visitor forecasts developed at regional, destination and individual market levels suggests that time series regression performs well in producing annual forecasts of visitors which can also serve as a baseline for evaluating the net returns from applying more complex techniques. Tourism managers should appreciate the usefulness of simpler formal methods in developing forecasts of visitors.