实用Box-Jenkins预测模型及应用

介绍 Box-Jenkins 的实用预测模型,并提出用随机模拟法模型的参数进行估计。与通常使用的最小二乘法相比,模拟法无需考虑初始值的选取,就能保证迭代过程的收敛性。整个程序的编制使用模块化结构,使预测者无需了解太多的这方面知识,就能方便地使用。在案例的预测过程中,只需进行几次人工干预,就能输出多种分析结果。     

论灰色道德

“灰色道德”是一种不求善不避恶，抛弃善恶两极，而走中间道路的道德中立观，它把妥协作为价值标准，容易导致人们同情邪恶，反抗美德，因失去道德标准、原则和目标而变得麻木，进而导致一个非理性社会的产生。崇尚选择灰色道德的人，不是不能够选择完全善和完全恶，而是不愿意选择完全善和完全恶。他们在心理上存在一种逃避心理，即追求道德责任的逃避，怕被烙上道德“黑”的污名。     

基于PDL模型的我国省域人口总量预测方法研究——以甘肃省为例

在我国省域人口总量预测领域，基于时间序列分析的PDL模型目前尚未得到充分使用。文章以甘肃省为例，运用混合有限PDL模型，通过其与分要素预测法之间的预测效果对比，对其在这一领域的应用进行尝试性的分析和探讨。     

基于加权案例推理模型族的软件成本SVR组合估算

精确地估算软件成本是软件项目成功开发的一个重要保证,直接影响着软件的风险控制和质量保证.为了更好地解决单一估算模型的不足,提出了集成多案例推理(CBR)模型的软件成本组合估算模型.首先,采用六种距离计算公式刻画新旧项目相似度,构建了六种CBR模型,并运用粒子群算法(PSO)来优化CBR模型族中的属性权重.其次,在CBR模型族的基础上,运用支持向量回归机(SVR)模型将不同CBR模型的估算结果进行集成,提高软件成本估算结果的精度.利用Deshamais数据库对模型有效性进行检验,实证结果表明,在六种CBR模型中Euc-CBR、Min-CBR、Gau-CBR和Mah-CBR模型估算结果没有明显差异,Gre-CBR和Man-CBR模型略优;提出的SVR组合估算模型估算精度明显优于单CBR模型和其他线性组合估算模型,能有效提高软件成本的估算精度.     

购买力平价理论在人民币汇率预测中的应用

购买力平价理论是汇率预测的重要方法。应用协整理论对人民币/美元汇率的购买力平价形式进行检验可以发现,购买力平价理论一定时期内在人民币汇率上不成立。应用神经网络技术构造的非线性协整检验,结果显示其预测的效果要明显优于随机游动模型。因此可以认为在引入了非线性的机制后,基于汇率理论的结构式模型在预测汇率变化的能力上有所提高。     

四川省科技人才需求预测研究

根据四川省经济发展速度和目标,从科技人才开发利用现状出发,利用回归系统经济预测模型,对四川省2005-2010年科技人才需求量进行了预测。并从社会经济发展的角度分析了科技人才使用效益和预测结果的可行性,最终得出了四川省科技人才队伍质量和创新能力不断提高的结论,但仍存在科技人才增速缓慢,相对数量较少的问题。     

Obtaining prediction intervals for FARIMA processes using the sieve bootstrap

The sieve bootstrap (SB) prediction intervals for invertible autoregressive moving average (ARMA) processes are constructed using resamples of residuals obtained by fitting a finite degree autoregressive approximation to the time series. The advantage of this approach is that it does not require the knowledge of the orders, p and q, associated with the ARMA(p, q) model. Up until recently, the application of this method has been limited to ARMA processes whose autoregressive polynomials do not have fractional unit roots. The authors, in a 2012 publication, introduced a version of the SB suitable for fractionally integrated autoregressive moving average (FARIMA (p,d,q)) processes with 0<d<0.5 and established its asymptotic validity. Herein, we study the finite sample properties this new method and compare its performance against an older method introduced by Bisaglia and Grigoletto in 2001. The sieve bootstrap (SB) method is a numerically simpler alternative to the older method which requires the estimation of p, d, and q at every bootstrap step. Monte-Carlo simulation studies, carried out under the assumption of normal, mixture of normals, and exponential distributions for the innovations, show near nominal coverages for short-term and long-term SB prediction intervals under most situations. In addition, the sieve bootstrap method yields better coverage and narrower intervals compared to the Bisaglia–Grigoletto method in some situations, especially when the error distribution is a mixture of normals.     

The Role of Statistical Models in the Prediction of Tropical Cyclone Motion

Tropical cyclones (tropical storms, hurricanes, typhoons, etc.) occur over many of the earth's tropical marine areas. Responsibility for tracking and predicting the future course of these storms is assigned to one or more domestic or foreign meteorological services. These services routinely activate a number of statistical and dynamical prediction models as objective guidance preparatory to issuing official forecasts on these storms. In this article, the role of the statistical models in this process is examined.     

Some aspects of probability forecasting

The problems of assessing, comparing and combining probability forecasts for a binary events sequence are considered. A Gaussian threshold model (analytically of closed form) is introduced which allows generation of different probability forecast sequences valid for the same events. Chi - squared type test statistics, and also a marginal-conditional method are proposed for the assessment problem, and an asymptotic normality result is given. A graphical method is developed for the comparison problem, based upon decomposing arbitrary proper scoring rules into certain elementary scoring functions. The special role of the logarithmic scoring rule is examined in the context of Neyman - Pearson theory.     

企业财务报表分析中的模糊数学方法研究

财务报表分析无论对于企业自身还是银行、政府监管部门和中介机构都是至关重要的。从财务报表数据的模糊性与传统分析方法的主观性问题出发，利用模糊多项式时间序列模型以及基于模糊聚类的模糊预测模型，进行财务报表的趋势分析和综合分析。实证研究表明，模型的精度较高，利用上述模型进行财务报表分析符合财务数据本身的特性，与已有文献相比，较好地解决了模糊参数的估计问题和模糊聚类模型的动态预测问题，将其应用于企业财务报表分析的实践是有一定实用价值的。