住房价格上涨与其影响因素之间的关系研究——基于VEC模型的实证分析

通过建立向量误差修正模型,对中国近年来住房价格持续上涨的原因进行了分析。结果表明,土地价格的快速上涨、金融对房地产业的过度支持以及在住房价格继续上涨预期刺激下的投机因素是中国住房价格上涨过快的主要原因。近年来中国住房价格的持续快速上涨在很大程度上是一种泡沫现象。     

Comparison of procedures for fitting the autoregressive order of a vector error correction model

This paper investigates the lag length selection problem of a vector error correction model by using a convergent information criterion and tools based on the Box–Pierce methodology recently proposed in the literature. The performances of these approaches for selecting the optimal lag length are compared via Monte Carlo experiments. The effects of misspecified deterministic trend or cointegrating rank on the lag length selection are studied. Noting that processes often exhibit nonlinearities, the cases of iid and conditionally heteroscedastic errors will be considered. Strategies that can avoid misleading situations are proposed.     

走出广告言辞表达的误区

〕本文着重对言辞表达不当导致广告误区的问题作出分析,并认为除语言本体的规范标准外,还必须用汉语文化精神和民族精神的尺度,去对广告言辞的是非优劣作出评判。     

从目的论视角探析企业简介翻译错误及其成因

根据Nord对翻译错误的分类和我们的调研,认为当前国内企业简介英语译文存在的问题主要有语用、文化和语言三方面错误。其主要原因在于译者缺乏明确的翻译目的,没能把译文读者放在首位,只是一味追求译文与原文在形式上的对等。企业简介翻译应当遵循三个原则：目的原则、译文读者导向原则和合适原则。     

错误分析理论在大学英语写作教学中的应用

写作教学在英语教学中占有十分重要的地位,运用错误分析理论对学生英语作文中的常见错误进行整理、分类和分析将对大学英语写作教学大有裨益。     

我国工业重工化对经济增长影响的实证分析

近些年来,我国出现了重新重工业化的趋势,重工业在我国经济中发挥着特殊重要的作用.采用1981-2006年的数据,在单位根检验、协整检验和格兰杰因果关系检验的基础上建立了误差修正模型,以此说明我国重工业与经济增长的长期均衡的正向关系和短期动态关系,指出我国应大力发展重工业,走一条新型工业化的道路.     

Confidence and prediction intervals based on interpolated records

In several statistical problems, nonparametric confidence intervals for population quantiles can be constructed and their coverage probabilities can be computed exactly, but cannot in general be rendered equal to a pre-determined level. The same difficulty arises for coverage probabilities of nonparametric prediction intervals for future observations. One solution to this difficulty is to interpolate between intervals which have the closest coverage probability from above and below to the pre-determined level. In this paper, confidence intervals for population quantiles are constructed based on interpolated upper and lower records. Subsequently, prediction intervals are obtained for future upper records based on interpolated upper records. Additionally, we derive upper bounds for the coverage error of these confidence and prediction intervals. Finally, our results are applied to some real data sets. Also, a comparison via a simulation study is done with similar classical intervals obtained before.     

Effect of aggregation on the estimation of trend in mortality

There are three approaches to analyzing and forecasting age‐specific mortality: (1) analyze age‐specific data directly, (2) analyze each cause‐specific mortality series separately and add the results, (3) analyze cause‐specific mortality series jointly and add the results. We show that if linear models are used for cause‐specific mortality, then the three approaches often give close results even when cause‐specific series are correlated. This result holds for cross‐correlations arising from random misclassification of deaths by cause, and also for certain patterns of systematic misclassification. It need not hold, if one or more causes serve as “leading indicators”; for the remaining causes, or if outside information is incorporated into forecasting either through expert judgment or formal statistical modeling. Under highly nonlinear models or in the presence of modeling error the result may also fail. The results are illustrated with U.S. age‐specific mortality data from 1968–1985. In some cases the aggregate forecasts appear to be the more credible ones.     

Regression from another angle

This paper presents a simply viewed framework that brings together various concepts of regression, prediction, and principal components. Several new concepts related to prediction are introduced, and then the interrelationships of these concepts are established. The generalizations are examined in detail and are illustrated in the context of a well known data set.     

Causality and interdependence analysis in linear econometric models with an application to fertility

This paper is an applied analysis of the causal structure of linear multi-equational econometric models. Its aim is to identify the kind of relationships linking the endogenous variables of the model, distinguishing between causal links and feedback loops. The investigation is first carried out within a deterministic framework and then moves on to show how the results may change inside a more realistic stochastic context. The causal analysis is then specifically applied to a linear simultaneous equation model explaining fertility rates. The analysis is carried out by means of a specific RATS programming code designed to show the specific nature of the relationships within the model.