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。     

环境法的需求与供给——一个法经济学视角

环境法是环境与资源要素相对价格提高、科技发展、人们对良好环境形成偏好条件下的产物。它的供给要受到所在国家利益格局、社会科学知识的积累、法律供给传统等诸要素的影响。在完善一个国家的环境法时，必须充分调动这些因素，以形成可持续发展。     

建立社会主义市场经济必须首先解放思想

从传统的计划经济向社会主义市场经济的转变，必须从计划经济体制下所形成的旧的思想观念中解放出来，树立适应社会主义市场经济的新观念，这是建立社会主义市场经济的需要。我国十几年的改革开放的实践已证明，在马克思主义的指导下，打破习惯势力和主观偏见的束缚，从实际出发，解放思想，是改革开放和进行社会主义建设的首要条件。     

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。     

开展项目教学,提高教与学质量

项目教学合理的选择了知识内容和教学方法,保证了学生获得相对完整的专业知识和专业能力,所以项目教学既提高了教师的教学素质,同时也提高了学生各方面的能力。     

机载GPS天线的研制

本文论述了作者最近研制的机载ＧＰＳ天线．使天线的波束宽度很宽，满足飞机在上３０°范围内翻滚要求，而能很好的地接收ＧＰＳ导航卫星的信号．实验证明使天线满足设计要求，天线性能是优良的．     

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.