Linear regression forecasting in the presence of ar(1) disturbances

This paper is concerned with time-series forecasting based on the linear regression model in the presence of AR(1) disturbances. The standard approach is to estimate the AR(1) parameter, ρ, and then construct forecasts assuming the estimated value is the true value. We introduce a new approach which can be viewed as a weighted average of predictions assuming different values of ρ. The weights are proportional to the marginal likelihood of ρ. A Monte Carlo experiment was conducted to compare the new method with five more conventional predictors. Its results suggest that the new approach has a distinct edge over existing procedures.     

基于协整分析法的我国环保投资预测

通过协整检验和格兰杰因果检验法证实了环保投资与3个宏观经济变量人均GDP、财政支出、工业增加值之间具有长期均衡的协整关系和因果关系．利用建立的回归方程对于我国“十二五”期间的环保投资总量进行了预测．结果显示,我国“十二五”期间预计环保投资将达到3．98万亿元．最后,根据研究的结果,提出“十二五”期间环保投资相应的政策建议．     

Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.     

预测未来的科学

“未来无法预测”的说法肯定是错误的。在科学基础上预测未来是常见的事,并不奇怪。典型例子如天气预报。而对地震的研究导致了地震学科的建立。客观世界遵循自然规律,但又是无规、随机、非线性和有突变的,因而预测未来既可能又很难成功。不能用因果性要求批评量子物理实验;可以用因果性对待经典物理实验,但不应死守“因先于果”的时序限制。因果律的精髓应是“在任何情况下果都不能影响因”。在Maxwell—d’Alembert方程求解过程中,完整的数学分析结果是有一个滞后解和一个超前解。过去的习惯作法是抛弃超前解而只留下滞后解,虽然从逻辑上的因果性出发而这样做并不合理,能被人们接受的理由只是“滞后解才与实验情况相符合”。现在已出现了众多的负波速实验,证明了超前渡存在,并且在实验中“果超前于因”。这些实验体现了“进入未来”的意蕴,或许可以看成是“时间机器”设想的某种体现。一般认为经过时空隧道可作时间旅行;也可利用量子力学现象,例如纠缠态和后选择。量子隐形传态传送的是量子态而非实体物质,从逻辑上讲量子后选择传态与时间旅行是一致的。因此旅行者实际上未动,而传送的是描写旅行者的全部信息。很明显,在量子理论中时间旅行是可能的。量子力学是未来学发展的动力。……然而未知事物大量存在,要承认未来充满了不确定性;对未来学的研究面临巨大困难,道路是漫长的。     

Economic and statistical measures of forecast accuracy

This paper argues in favour of a closer link between the decision and the forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued to be used with meteorological forecasts, it is hardly mentioned in standard academic texts on economic forecasting. Some of the main issues involved are illustrated in the context of a two‐state, two‐action decision problem as well as in a more general setting. Relationships between statistical and economic methods of forecast evaluation are discussed and links between the Kuipers score used as a measure of forecast accuracy in the meteorology literature and the market timing tests used in finance are established. An empirical application to the problem of stock market predictability is also provided, and the conditions under which such predictability could be explained in the presence of transaction costs are discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

基因表达编程算法的冻融系数预测研究

冻土融沉系数是冻土融化后沉降量的直接反映,影响融沉系数的主要因素有粉黏粒含量、未冻土的含水率、干密度、液塑限等.为了定量化的描述融沉系数与各影响因素之间的关系,引入基因表达式编程算法进行五维空间上的函数挖掘,将函数表达式基因化,通过Karva语言对基因进行编码,利用达尔文的进化理论,初始种群经过若干代的交叉变异,最终得出了最优化的个体,即最优函数表达式.结果表明:得到的函数表达式能综合反映各因素与融沉系数之间的关系;对测试样本进行预测,R2值为99.50%,实现了融沉系数预测的高精度和直观化,为融沉系数预测提供了一种新的智能化方法.     

基于GIS空间分析的台风路径预测

建立了一种利用历史数据进行台风路径预测的模型。该模型应用关键点相似度法,利用地理信息系统(GIS)的空间分析功能,建立当前实测台风中心位置的缓冲区,从历史台风数据库中搜索路径相似的台风,确定各条路径的相似度;再依据相似度确定台风的相似权重,进行24~48 h的台风路径趋势的快速预测;根据每6 h的预测结果对预测结果进行修正。实例研究表明,24 h预测误差小于140 km,48 h预测误差小于240 km。     

Argo——成功的十年

由美、日等国科学家发起的国际Argo计划已经走过了10个年头,由3000多个自动剖面浮标组成的全球Argo实时海洋观测网也已于2007年10月末正式建成,成为全球海洋观测系统的重要支柱,迄今在全球海洋0—2000m深度范围内获取的温度、盐度和海流剖面资料总数已达56万余条,并正以每年10万条剖面以上的速度增加。随着Argo资料数量的快速增加和质量的不断提高。它们在海洋和大气等多个领域科学研究和业务活动中的应用也得到了长足发展,正有效地改变着人们对许多重大自然环境问题的认识,提高了人们对重大海洋和天气事件的预测预报能力。我国于2001年正式加入该计划,已经初步建立起我国的Argo大洋观测网,并在Argo资料的接收、处理和质量控制等方面积累了丰富的经验。但是,与其他国家相比,无论在重视程度和投入力度方面都还存在较大的差距。急需整合资源和力量,强化我国Argo大洋观测网的建设及其资料的应用研究,为应对全球气候变化做出更大的贡献。     

城市用水量预测的混沌理论研究

分析了混沌理论特征,利用城市用水量混沌特性,建立城市用水量短期预测模型,对城市用水量进行科学预测.利用历史数据信息,在相空间重构基础上对城市用水量时间序列进行分析,分析饱和嵌入维数、延迟时间和Lyapunov指数求解方法并对其进行计算,并以此为指导对城市用水量进行高精度预测.利用建立的模型对东北某市日用水量进行预测,结果表明基于混沌理论的城市用水量预测模型具有较高精度,对于受众多因素影响的城市用水量预测有良好的推广价值.城市用水量受众多因素影响,混沌理论为城市用水量预测提供了崭新思路.     

预测模型组合开关

不同预测模型在同一时刻都存在不同的预测误差,最优组合预测的预测精度要比其他模型高,但并不是所有时刻误差都最小,有的时刻可能比单项预测误差还大,因此,在预测时就存在较大的风险.再组合预测可以降低预测风险,但方法比较复杂难以推广.针对这些问题,我们设计一种预测模型组合开关,根据数据的变化,不同时刻采用不同预测模型,使预测误差每一期都最小,或不是最大.实证结果表明,预测模型开关能够根据数据特征有效控制模型输出.