Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

Forecasting in long horizons using smoothed direct forecast

This paper constructs a forecast method that obtains long‐horizon forecasts with improved performance through modification of the direct forecast approach. Direct forecasts are more robust to model misspecification compared to iterated forecasts, which makes them preferable in long horizons. However, direct forecast estimates tend to have jagged shapes across horizons. Our forecast method aims to “smooth out” erratic estimates across horizons while maintaining the robust aspect of direct forecasts through ridge regression, which is a restricted regression on the first differences of regression coefficients. The forecasts are compared to the conventional iterated and direct forecasts in two empirical applications: real oil prices and US macroeconomic series. In both applications, our method shows improvement over direct forecasts.     

基于ESI和InCites的学科发展预测研究--以中国地质大学为例

基于汤姆路透公司的ESI和InCites数据库，分析了中国地质大学各学科的特征，探讨了高被引论文、热门论文、顶尖论文与学科发展的关系，并预测后续最有可能入围ESI世界前1％的学科。     

基于变量选择-神经网络模型的复杂路网短时交通流预测

摘要： 针对传统交通流预测模型正在由单断面历史数据处理向多断面、多时刻历史数据处理转变,但在考虑各断面间的影响时,多变的交通状况往往会使预测模型复杂化的问题,引入一种多元线性回归最小绝对收缩和选择算子方法(Lasso),并利用其优秀的变量选择能力,在复杂路网多断面中选出相关性较高的断面；结合神经网络(NN)的非线性特性,提出了Lasso NN组合模型.结果表明：Lasso NN模型在路网交叉口对未来15 min交通流数据预测的误差率低于9.2%；在非交叉口的误差率低于6.7%,总体优于各自单独使用得出的结果.     

PXI在测控系统中的应用

随着有关PXI总线的规范和产品的推出，由于优良的性能指标和适宜的价格，使其受到各相关行业的广泛关注。对风洞测控系统的结构和系统配置作了具体的阐述，并对PXI总线在具体使用中的效果和特点作了较详细的介绍。     

La2分子结构与基态分子势能函数

用相对论有效原子实势，在密度泛函理论(DFT)和组态相关理论(QCISD)水平上优化计算了La2分子的结构．根据原子分子反应静力学原理，导出La2分子可能的电子状态和离解极限，用DFT中的B3LYP方法计算了基态X1∑g^ 势能曲线，拟合得到Murrell-Sorbie解析势能函数，并首次计算得到基态X1∑g^ 的谐振频率、热力学性质、力学与光谱数据。     

泵站运行效率分析与节能措施

从泵站的电耗入手，讨论和分析了泵站机组实际运行工况与设计工况的偏离．并通过机组电耗和效率分析，提出了若干节能措施。     

如何发挥举报在反腐败中的作用

举报是宪法赋予公民的权利,举报在反腐败工作中发挥着重要作用,检察机关查办案件大量线索都来源于群众举报,但现实情况下存在着不敢实名举报等许多令人担忧的问题,造成这一现状的主要原因是害怕打击报复。针对这些问题,作者提出要给举报人以实质性的保护,采取和推广网上举报等举措,以更好地发挥举报在反腐败中的作用。     

面向MIS的预测支持系统的研究

通过对预测支持系统研究现状的分析,讨论了预测支持系统如何更好的适用于MIS,为其提供预测支持.并提出了更加适应MIS当今和未来发展的预测支持系统构建模式.     

基于GM（1，1）模型的某高校招生人数预测

分析了近年来我国高校招生人数的变化趋势，并利用GM（1，1）模型对某高校2008年的招生人数进行了预测．从而为高校招生人数决策提供了依据。