金融结构对产业结构影响的时变特征研究——基于SUR和TVPSS模型的实证检验

金融结构与产业结构的关系一直是学术界的研究热点.文章利用中国1998—2017年的年度数据,构建似不相关回归模型从金融结构规模、效率及深化的角度分析金融结构对产业结构合理化和高级化的影响,建立时变参数状态空间模型描绘了金融结构对产业结构合理化和高级化的动态冲击.实证分析结果表明:金融结构规模提高产业结构合理化水平,促进了产业结构高级化;金融结构效率提高产业结构合理化水平,抑制了产业结构高级化;金融结构深化降低产业结构合理化水平,促进了产业结构高级化.金融结构规模、金融结构效率及金融结构深化对产业结构合理化和高级化的冲击均呈现出时变特征;金融结构对产业结构合理化的影响滞后于其对产业结构高级化的影响.金融结构对产业结构的冲击波幅呈现出前期波动大、后期较为平缓的状态,部分金融结构变量对产业结构的动态冲击呈现出"长尾"现象.当前的中国金融结构已经不适合当前的产业结构,需调整金融结构,以提升产业结构合理化水平和高级化水平.     

心律波动信号的1/f噪声特性分析

心律波动信号具有1/f噪声特性,是非平稳的。该文采用平均功率谱密度方法,分析了基于平均功率谱密度和最小二乘拟合法求正常人和心脏充血患者的心律波动信号的谱参数,发现根据谱参数可以定量地区分正常人与心脏充血患者:大部分正常人的心律波动信号的谱参数大于1,而大部分心脏充血患者的心律波动信号的谱参数小于1,该结论可为心脏病的诊断与预诊断提供一种辅助手段。     

娄烦方言语气词“哩”的功能及参项意义

文章应用变换分析法,多角度分析了娄烦方言语气词“哩”单一的标志现在完成或实现的功能,确定了它在句子中的句法地位。并以此为基础,分析了其标志域(INDICATIVESCALE)。最后根据语气词标志域语义性质特点,确定谓语分类的两个参项,并建立测试框架,从形式上得以验证,以此来说明娄烦方言语气词的参项意义。以期对娄烦方言语气词“哩”做全面的描写和分析。     

网络社会公众同情形成过程中的媒介作用机制研究

在网络社会的社会实践过程中,信息传递的充分和对称并没有避免公众同情与事实本身形成偏差的出现。由于信息技术改变了人们的生活方式,传播网络上的发声成为了节点化生活中的人们参与社会事务的主要手段。社会资本运动的核心转变为文化资本,公众同情作为传播过程中核心节点——话题序参量媒体参与社会资本活动的重要工具,在公共舆论事件的发展和演变过程中起着重要的作用。     

基于时变参数的中国总量生产函数估计

总量生产函数是宏观经济增长分析和技术进步定量化测算的基础,传统方法估算总量生产函数具有很多不足之处。利用1952—2005年中国宏观经济数据,建立时变参数模型,应用卡尔曼滤波算法,对我国1952—2005年间历年的总量生产函数进行了估算。研究结果表明:利用时变参数估算生产函数较好地捕捉到了经济政策的重大事件对于总量生产函数的影响,可能是理论与实际情况更加接近的一种拟合。我国技术进步和资本、劳动的产出弹性三者相互影响,呈同方向变化,并且技术进步对资本的产出弹性影响较大,对劳动的产出弹性影响较小,1978—2005年间我国属于节约资本型技术进步。     

指数分布参数基于不完全数据的区间估计

对不完全样本观测数据,讨论了指数分布总体参数的区间估计;给出了构造置信区间的一种方法并推导出了相应的分布密度函数表达式;并说明了该方法在样本中可能存在异常值时的应用。     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

Model selection for Lévy measures in diffusion processes with jumps from discrete observations

We deal with parametric inference and selection problems for jump components in discretely observed diffusion processes with jumps. We prepare several competing parametric models for the Lévy measure that might be misspecified, and select the best model from the aspect of information criteria. We construct quasi-information criteria (QIC), which are approximations of the information criteria based on continuous observations.     

Finite normal mixture copulas for multivariate discrete data modeling

A new family of copulas is introduced that provides flexible dependence structure while being tractable and simple to use for multivariate discrete data modeling. The construction exploits finite mixtures of uncorrelated normal distributions. Accordingly, the cumulative distribution function is simply the product of univariate normal distributions. At the same time, however, the mixing operation introduces association. The properties of the new family of copulas are examined and a concrete application is used to show its applicability.     

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model. Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the r$r$th moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin et al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214–223]. The usefulness of these models is illustrated in a simulation study and in applications to three real data sets.