Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

黑龙江省伊春市五营国家森林公园负离子研究

根据伊春市五营国家森林公园中的空气负氧离子浓度的实测资料,利用空气离子单极系数及空气离子评价系数进行了评价和研究.结果表明,该森林公园中空气负离子含量达到国际旅游度假区一级标准,空气清洁度属于A级“最清洁”;在动态水周围的一定范围内随着与动态水的距离的逐渐增大,空气离子单极系数大致呈线性增加趋势;空气离子评价系数值大致呈线性减小趋势.而在这个范围之外,两个值都将趋于平缓;在分别以红松、冷杉和白桦三种树种为主体的纯林中,空气清洁度均在早晚时比较高,在中午时出现低谷.     

南淝河水环境质量的改进密切值法综合评价

密切值法是多目标决策中的一种优选法,它适用于多测点、多指标的质量评价;水环境是一个多因素、多因子的复合体,因此可以用密切值法来进行评价。文章对合肥市南淝河水环境质量用密切值法进行了综合评价,评价项目取该河14个监测点的主要污染物CODcr、TP及NH3-N等3项监测指标作为评价因子,得出了评价结果并对污染程度进行了排序;与向量模评价法进行比较,结果表明该方法切实可行。     

码头项目环境风险评价方法探讨

近年来,环境风险评价在环境影响评价中逐渐引起重视,通过对实际工作中码头建设项目风险评价实例的研究,试图对环境风险评价的概念和方法进行探讨.     

云仿真系统可信度评估问题探讨

云仿真平台支持“云仿真”模式,能够自动查找仿真资源、动态构建云仿真系统。由于云仿真平台具有按需构建仿真服务、多粒度资源共享等特点,云仿真系统的可信度评估工作在仿真资源的可信度、仿真子系统的可信度、仿真运行环境的可靠性等方面面临新的问题。从云仿真系统全生命周期角度,建立了云仿真系统的可信度评估过程模型和云仿真系统可信度评估指标体系,并给出了一种仿真资源排序评估方法,为云仿真系统可信度评估工作提供参考。     

基于深度贝叶斯网络学习的不确定性建模方法

战场态势评估涉及很多不确定因素,对不确定性进行仿真建模能够提高态势评估的能力。针对参战对象多元、不确定性增多导致的无法全面准确表达不确定性问题,提出了基于记忆模块和变分自编码器的深度贝叶斯网络模型。采用生成模型设计了基于深度贝叶斯网络学习的态势评估模型;阐述了融合记忆模块的深度生成模型原理和模型的学习与推理过程;以某空袭行动为例构建贝叶斯网络,对所提方法进行了验证。结果表明:深度神经网络能够逼近隐变量的非线性变换,设计的记忆模块能存储深度神经网络提取的大量局部特征,通过学习自动得到了贝叶斯网络条件概率,增强了不确定性建模能力。     

企业中行政人员绩效考评问题的思考

利用"价值链分析法"对企业中行政人员的概念进行划分,以明确行政人员和企业其他员工的不同。对企业中行政人员的工作特点绩效考评易出现的工作分析不完善、考核标准不明确、缺乏沟通和反馈等几大问题提出了对行政人员进行科学分析、考核目标和实际工作岗位相结合。考核要公开公正等对策建议。     

基于故障树分析和贝叶斯网络方法的液压举升机构故障诊断

为应对液压举升机故障原因复杂,诊断方法准确性不高等问题,提出一种基于故障树和贝叶斯网络的液压举升机故障诊断方法。首先建立液压举升机构故障树,然后将故障树转换为贝叶斯网络,利用三角模糊函数表示举升机底事件发生概率,得到底事件模糊概率,将其做为先验概率计算叶节点发生概率,进而求得根节点后验概率以及概率重要度,可快速诊断出故障点。     

基于人民币升值广义事件窗的美元/人民币汇率风险评价

本文选取了近两年来美元/人民币的日汇率样本作为广义事件窗中的样本信息,以2005年7月21日这一日期为时间分界点.分别研究人民币升值前后即广义事件窗中该汇率的各种统计特性,利用非线性时间序列模型的方法,分别对汇率的收益率序列建立合适的模型,用以分析人民币升值前后汇率风险水平的变化,即汇率风险评价.