基于灰色理论的中国人口空间分布模式预测

运用灰色理论对我国各个省市未来人口总量进行预测,并应用GIS的空间自相关理论和方法对未来人口空间分布模式做出预测。研究认为,从2005年到2020年中:北京、天津、江苏、上海、浙江等省市始终保持与周围省市人口密度的高高关联关系,围绕这些省市的省份区域形成了相对人口聚集区域,西部许多省份及东北几省与周围的省份人口密度是维持着低低关联模式。广东省始终保持与周围省市人口密度的高高关联关系,即本省只是单独形成了一个聚集点。2014年是我国人口空间分布格局变动较大的时期。     

The future of hydrogen fueling systems for transportation: An application of perspective-based scenario analysis using the analytic hierarchy process

This paper integrates the analytic hierarchy process (AHP) with scenario analysis techniques to explore the commercialization of future hydrogen fuel processor technologies. AHP is a multi-attribute decision analysis tool useful for evaluating decisions with multiple criteria and alternatives. In this paper, AHP is extended using a technique called perspective-based scenario analysis (PBSA). In PBSA, scenario analysis is conducted based on potential future decision-maker perspectives that are integrated into the AHP framework. This paper discusses this method and applies it to the evaluation of hydrogen fuel processor technologies 15–20 years hence. The results provide an added layer of insight into the opportunities and barriers for the commercialization of these technologies as well as the methodological opportunities for using AHP and PBSA as a futures tool.     

证券投资风险预测的E-Bayes估计

本文给出了E-Bayes方法，以上海证券个股五粮液52个连续交易日的收盘价格为例，建立数学模型进行分析和预测，预测结果与市场实际值相当吻合。与灰色系统理论中的GM（1，1）预测模型相比，本文提出的方法预测的精度更高，计算量小。不仅适用于经济系统的分析与预测，也适用于其它系统的分析与预测。     

带有结构突变的单位根检验——文献综述

单位根检验是时间序列分析的基础,而是否考虑结构突变对单位根检验的结论有着重要影响,因此,考虑结构突变的单位根检验已成为计量经济学界的一个前沿热点问题。本文回顾了这一问题的发展历史,总结了该领域已取得的一些重要研究成果,最后对该问题最新的发展动向加以概括。     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

The Delphi method, impeachment and terrorism: Accuracies of short-range forecasts for volatile world events

Two experiments examined the effects of iterated polling and feedback on prediction accuracy for volatile world events using the Delphi paradigm. In both experiments, participants predicted rapidly changing geopolitical events. The first experiment occurred during the litigation surrounding the Clinton/Lewinsky affair in 1998. Results indicated that feedback improved individual and group accuracy for predictions of whether an event would occur. These types of predictions derived from group consensus were approximately 90% accurate, which was significantly higher than the average individual participant's accuracy. Neither polling nor feedback had much effect on time course predictions. The second experiment occurred during the American military action against the Taliban regime in Afghanistan in 2001. In Experiment 2, participants were polled three times to determine if increasing the number of iterated polls would improve Delphi accuracy. The results showed that accuracy improved from the first to the second poll but not from the second to the third. The groups predicted whether these scenarios would occur with approximately 75% accuracy, which was significantly higher than the accuracy of any individual participant. Once again, the Delphi procedure did not enhance time course predictions in Experiment 2.     

基于时间序列模型在物流需求预测中的应用

本文采用时间序列模型进行物流需求预测，用实例从数据平稳性的判断，平稳化、标准化，建模，经模型的识别、定价、参数估计和检验，到预测及误差和置信区间的计算，详细地说明了时间序列模型在物流需求预测中是如何应用的。结果表明该ARMA模型能够较好地拟合并可获得较高的中短期预测精度。     

外债风险预警模型及中国金融安全状况评估

本文提出了一个用于预警一国外债风险的动态模型 ,即多元累计和模型。模型的用户 (债权人和债务国 )能很早地预测到可能导致债务国重订债务期限的金融危机。实证分析结果表明 ,模型具有提前 3年探测到债务国潜在的还债困难的能力。对中国经济金融安全状况的评估结果表明 ,模型可提前 1年发出预警信号。     

基于"标杆管理"的海南远景经济指标预测

在海南远期经济指标的预测过程中,时间跨度较大,经济社会发展的不确定因素很多,利用传统的数学模型不易把握其发展态势,难以预测。借鉴管理学上“标杆管理”的模式成功地对海南远景经济指标进行了分析预测,该思路同样适用于其他区域经济远景指标的预测。     

2000～2018年乡村教师队伍建设研究热点与演化趋势研究

从CNKI数据库收录的2000—2018年文献中,筛选出2148篇"乡村教师队伍建设"领域相关文献,利用CiteSpace和SA-TI3.1软件,对样本文献外部特征、关键词共现、知识演进趋势与热点前沿展开知识图谱分析。首先,展示了乡村教师队伍建设研究现状,梳理了发展动态。其次,划分了"科学发展观""教师教育""特岗教师"和"义务教育管理体制"四大热点研究领域。在此基础上将乡村教师队伍建设研究分为3个演进阶段,并总结出"农村小规模学校""乡村教师专业发展""特岗教师"和"教师流动"四个方向为该领域的前沿热点。最后,分别从公费师范生、基础设施建设和乡村教师的幸福感等方面获得了一些启示。