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.     

浅谈功率因数的补偿及其应用

根据我国当前电力系统运行情况，对电网无功功率补偿的必要性及通用补偿方法及其装置进行了具体阐述。     

建筑物地基处理的设计与施工探讨

以具体工程为例，介绍了建筑物地基处理的设计、施工中遵循的原则，施工中遇到特殊情况时采取的处理方法和必要的检测方法，对几种桩基的设计要求、施工工艺及过程、质量控制等情况进行了简单阐述。     

顶凌覆盖节水增产技术研究

通过1998年、2000年的试验研究，系统分析了顶凌覆盖技术在高寒区节水增温保温、增产增收的机理及其应用效果。提出该技术可以缓解低温干旱特别是春旱对农业生产的困扰，是一项可以抗旱保春播增产增收的实用技术，在高寒地区具有较高的推广价值。     

Solving Non-Uniqueness in Agglomerative Hierarchical Clustering Using Multidendrograms

In agglomerative hierarchical clustering, pair-group methods suffer from a problem of non-uniqueness when two or more distances between different clusters coincide during the amalgamation process. The traditional approach for solving this drawback has been to take any arbitrary criterion in order to break ties between distances, which results in different hierarchical classifications depending on the criterion followed. In this article we propose a variable-group algorithm that consists in grouping more than two clusters at the same time when ties occur. We give a tree representation for the results of the algorithm, which we call a multidendrogram, as well as a generalization of the Lance andWilliams’ formula which enables the implementation of the algorithm in a recursive way. The authors thank A. Arenas for discussion and helpful comments. This work was partially supported by DGES of the Spanish Government Project No. FIS2006–13321–C02–02 and by a grant of Universitat Rovira i Virgili.