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。     

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。     

光引发丙烯酰胺沉淀聚合研究

以丙酮/水为溶剂，采用紫外光（UV）引发丙烯酰胺（AM）沉淀聚合，考察了聚合反应的特征以及溶剂、初始单体浓度、引发剂浓度、表面入射光强、液层厚度等参数对聚合物分子量的影响。结果表明，紫外光引发丙烯酰胺沉淀聚合过程中单体转化率和聚合物分子量都随反应时间的延长而增加，光照80min时，单体转化率可达90％以上。所得聚合物分子量为10⁵～10⁶，提高单体浓度可使聚合物分子量增加，增加引发剂浓度和提高光强导致聚合物分子量降低，液层厚度对聚合物的分子量影响不大。所得聚合物粒子粒径约为200～300nm左右，粒径分布较为均匀，溶剂组成对聚合物粒子相貌影响较大，增加水含量可使粒子粒经变大但分散性变差。     

一类时滞系统的模糊预测控制方法

对模糊控制和预测控制相结合的控制方法进行研究,提出了一种简单易于实现的模糊预测控制的组合方法.此方法将模糊控制的定性分析特点和预测控制的定量分析特点有效地结合起来,同时积分器的引入可以消除稳态偏差.仿真结果表明,该方法对控制作用的滞后时间不确定的系统十分有效.     

A NEW DESCRIPA NEW DESCRIPTOR OF AMINO ACIDS BASED ON THE THREEDIMENSIONAL VECTOR OF ATOMIC INTERACTION FIELD

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) IS A VERY IMPORTANT TOPIC FOR MANY SCIENTIFIC FIELDS AND CAN BE TERMED AS QUANTITATIVE SEQUENCE ACTIVITY MODELS(QSAM)[1] WHEN REFERRED TO THE RESEARCH ON RELATION- SHIPS BETWEEN STRUCTURES AND ACTIVITIES OF BIOLOGICAL MOLECULES, SUCH AS PROTEINS, AND NUCLEIC ACIDS. HOW- EVER, IT IS VERY DIFFICULT TO ESTABLISH A QSAM MODEL,…     

Forecast accuracy after pretesting with an application to the stock market

In econometrics, as a rule, the same data set is used to select the model and, conditional on the selected model, to forecast. However, one typically reports the properties of the (conditional) forecast, ignoring the fact that its properties are affected by the model selection (pretesting). This is wrong, and in this paper we show that the error can be substantial. We obtain explicit expressions for this error. To illustrate the theory we consider a regression approach to stock market forecasting, and show that the standard predictions ignoring pretesting are much less robust than naive econometrics might suggest. We also propose a forecast procedure based on the ‘neutral Laplace estimator’, which leads to an improvement over standard model selection procedures. Copyright © 2004 John Wiley & Sons, Ltd.     

脂肪腈结构与凝聚型性能之间定量关系的研究

借助于分子拓扑学探讨了脂肪腈的凝聚型性能与分子结构之间的关系，提出一个结构基础明确的定量关系式．对乙腈到二十一烷腈的计算结果表明，沸点、密度和折光指数的计算值都很接近实验值．应用这一定量关系，不仅能够合理表征脂肪睛结构与凝聚型性能的关系，而且有助于揭示物质结构与性能关系之间的奥秘。