信心、货币政策与中国经济波动关系的统计检验

文章基于时变视角,依据行为经济学理论,构建了一个包含企业家信心、投资者信心、利率、货币增长率、经济增长率和通货膨胀率六变量的TVP-VAR模型,研究信心、货币政策与经济波动之间的时变特征。结果表明:企业家信心和投资者信心能够影响利率和货币增长率,即信心可以通过影响货币政策进而作用于宏观经济。货币增长率和利率能够影响投资者信心和企业家信心,进而可以通过信心影响宏观经济。从时变角度看,企业家信心一单位的正向冲击在整个样本区间内均会促进经济的增长;在短期内投资者信心和货币增长率对经济增长具有促进作用,但是在长期内货币增长率的提高会阻碍经济的增长。     

重塑学生自信心的情感教学法初探

现代教育培养的人才应具有健全的人格、较强的创新精神和实践能力,善于协作和敢于竞争.然而,目前高校中有相当一部分学生因为学习成绩差等原因而丧失自信心,陷入不良情感的心理障碍中不能自拔.基于人本主义学习理论的情感教学法注重教学过程中的师生互动和情感交流,能够有效地促使学生重塑自信心,恢复积极向上的健康心态.情感教学法可从五个环节来实施:(1)简介课程的概况,确立课程的学习目标,对学生进行积极的鼓励;(2)调查学生的情况,将学生进行合理分组;(3)在教学过程中将课程知识传授、能力培养与信心培养相结合;(4)实行阶段考察,鼓励学生进一步学习;(5)课程结束时对"差生"实施积极的补救措施.     

中国消费者信心影响因素实证分析

消费者信心是对消费者整体所表现出来的信心程度及其变动的一种测度。使用离散顺序选择模型对2009年第1季度中国大陆消费者信心指数的原始调查数据进行实证分析,结果显示：消费者对未来经济发展的信心受到其对未来就业、收入、生活和投资四个方面信心的显著影响。     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.     

国家文化公园的“国家性”建构研究

建设国家文化公园是以习近平同志为核心的党中央作出的重大决策部署,是推动新时代文化繁荣发展,推进文化强国建设的重大文化工程,是实现物质与精神共同富裕的“国之大者”。国家文化公园的建设过程本质上是国家文化标识建构与文化空间生产的过程,“国家性”在其整体定位、建构意义与实现路径三个方面贯彻始终,并指导着国家文化公园的建设。“国家性”是国家文化公园的鲜明底色,也是建设国家文化公园的基石。因此,推进“国家性”建构是国家文化公园建设中的重要着力点。     

消费者网络购买认可度影响因素分析

文章以具有网络购物行为的网民为样本,利用SPSS软件对影响网络购买认可度的相关因素进行了效度分析、信度分析和相关性检验;分析这些因素对于购物网站认可度的影响作用,并提出了提高网站购买认可度的措施。     

Asymptotic Properties for a Class of Partially Identified Models

We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV). An SVRV is a mapping that associates a set (rather than a real number) with each element of the sample space. Examples of population features in this class include interval‐identified scalar parameters, best linear predictors with interval outcome data, and parameters of semiparametric binary models with interval regressor data. We extend the analogy principle to SVRVs and show that the sample analog estimator of the population identification region is given by a transformation of a Minkowski average of SVRVs. Using the results of the mathematics literature on SVRVs, we show that this estimator converges in probability to the population identification region with respect to the Hausdorff distance. We then show that the Hausdorff distance and the directed Hausdorff distance between the population identification region and the estimator, when properly normalized by , converge in distribution to functions of a Gaussian process whose covariance kernel depends on parameters of the population identification region. We provide consistent bootstrap procedures to approximate these limiting distributions. Using similar arguments as those applied for vector valued random variables, we develop a methodology to test assumptions about the true identification region and its subsets. We show that these results can be used to construct a confidence collection and a directed confidence collection. Those are (respectively) collection of sets that, when specified as a null hypothesis for the true value (a subset of values) of the population identification region, cannot be rejected by our tests.     

On the Validity of the Bootstrap in Non‐Parametric Functional Regression

Abstract. We consider the functional non‐parametric regression model Y= r( χ )+?, where the response Y is univariate, χ is a functional covariate (i.e. valued in some infinite‐dimensional space), and the error ? satisfies E(? | χ ) = 0. For this model, the pointwise asymptotic normality of a kernel estimator of r (·) has been proved in the literature. To use this result for building pointwise confidence intervals for r (·), the asymptotic variance and bias of need to be estimated. However, the functional covariate setting makes this task very hard. To circumvent the estimation of these quantities, we propose to use a bootstrap procedure to approximate the distribution of . Both a naive and a wild bootstrap procedure are studied, and their asymptotic validity is proved. The obtained consistency results are discussed from a practical point of view via a simulation study. Finally, the wild bootstrap procedure is applied to a food industry quality problem to compute pointwise confidence intervals.     

极值理论高频VaR区间估计模型的构建

为了对在险值的估计精度进行度量,更为精确和有效地衡量极值VaR（value at risk）的估计风险,基于广义极值理论构建了极值VaR的区间估计模型,并进一步利用高频数据重点考察了不同置信水平和不同样本容量分块下的极值VaR区间估计结果的精度和模型的有效性。结果表明,极值VaR的动态区间估计模型与参数法和非参数法区间估计模型相比,不仅能够更为有效地捕获极端条件下收益率时间序列的动态特征,而且具有很好的估计精度,VaR估计风险的精确度更高。