数字化时代网络信息传播秩序重构的内在逻辑与行动策略

数字技术使信息传播渠道从单一向“网络立体化”转变,信息传播主体从“中心化”向“分散化”转变,信息传播环境从“真实化”向“拟态化”转变,信息传播出现了“混沌”与“失序”现象。“坚持人本理念”是网络信息传播秩序重构的基本逻辑,“信息资源与要素的再整合”是网络信息传播秩序重构的基础条件,“智能化信息平台决策机制”是网络信息传播秩序重构的保障。在信息传播秩序重构的过程中,需要强化技术体系与网络体系的链接,搭建多层次信息流动与传播的网络空间;需要加强对社会生活空间与产业的数字化改造,促使现实世界向虚拟世界迁移;需要催生全新的元宇宙信息场域,构建与现实世界映射与交互的虚拟世界。     

数字化发展、制度环境与政府舆情治理质量

基于2016—2020年我国31个省级行政区的面板数据,考察数字化发展、制度环境与政府舆情治理质量之间的关系。研究发现,数字化发展对政府舆情治理质量具有改善效应;相比制度环境较差的省份,数字化发展对舆情治理质量的改善效应在制度环境较好的省份更为显著。异质性检验表明,数字化发展对政府舆情治理质量的改善效应在数字化水平高的省份、制度环境较好但数字化水平低的省份显著,在制度环境较好的省份、制度环境较差的东部省份显著。研究结论为借助数字化发展改善政府舆情治理质量提供了理论依据。     

理论自觉：构建马克思主义中国化研究范式的内在要求

按照马克思主义的本质要求和当代中国社会发展的客观需要,研究者在马克思主义中国化研究范式问题上坚持历史使命的时代自觉、范式价值的学术自觉、整体构建的原则自觉与问题导向的实践自觉的高度统一和有机融合,对于实现研究使命、形成研究规范、促进马克思主义中国化的创新和发展具有重大价值。     

城市韧性与城市宜居性耦合协调研究——以长三角城市群为例

文章基于2010—2020年长三角城市群内26个城市的面板数据,结合长三角城市群城市韧性和宜居性发展现状,利用熵值法分别测算二者综合发展指数,在此基础上,进一步运用耦合协调模型进行定量分析。结果表明：长三角城市韧性与城市宜居性总体水平不断上升,相对而言,城市宜居性的发展水平要略高于城市韧性;两系统耦合协调指数较高,总体呈现出相对稳定的态势,区域内部上海耦合协调度保持在最高水平,协调城市类型主要以初级和中级协调为主;空间格局上耦合协调城市板块呈现出环绕式聚集分布。据此,提出通过加强韧性建设来提高城市抵御风险的能力、因地制宜以特色发展打造宜居韧性城市、城市转型实现长三角城市群协调发展等政策建议,进而推动长三角城市群整体综合发展能力,实现以点带面辐射整个城市群。     

民族工作时代主题与加快民族发展问题研究

民族工作必须体现时代的精神,党中央在我国社会主义建设的关键时期提出的各民族需“共同团结奋斗,共同繁荣发展”,顺应了时代的要求。少数民族的发展只有在建设中国特色社会主义的共同事业中才能逐步解决。     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

On comparing,classifying and clustering several dependent regression models

Classifying several regression models fitted on a dataset is one of the most problems in data analysis. In other words, scientists are interested in comparing several regression models that can be used for a dataset. In this paper, an approach will be used to compare and classify several dependent regression models. Then the performance of the proposed method is investigated using simulation study and real example.     

A new estimator of Kullback–Leibler information and its application in goodness of fit tests*

We propose here a general statistic for the goodness of fit test of statistical distributions. The proposed statistic is constructed based on an estimate of Kullback–Leibler information. The proposed test is consistent and the limiting distribution of the test statistic is derived. Then, the established results are used to introduce goodness of fit tests for the normal, exponential, Laplace and Weibull distributions. A simulation study is carried out for examining the power of the proposed test and to compare it with those of some existing procedures. Finally, some illustrative examples are presented and analysed, and concluding comments are made.     

A misspecification test for the higher order co-moments of the factor model

The traditional estimation of higher order co-moments of non-normal random variables by the sample analog of the expectation faces a curse of dimensionality, as the number of parameters increases steeply when the dimension increases. Imposing a factor structure on the process solves this problem; however, it leads to the challenging task of selecting an appropriate factor model. This paper contributes by proposing a test that exploits the following feature: when the factor model is correctly specified, the higher order co-moments of the unexplained return variation are sparse. It recommends a general to specific approach for selecting the factor model by choosing the most parsimonious specification for which the sparsity assumption is satisfied. This approach uses a Wald or Gumbel test statistic for testing the joint statistical significance of the co-moments that are zero when the factor model is correctly specified. The asymptotic distribution of the test is derived. An extensive simulation study confirms the good finite sample properties of the approach. This paper illustrates the practical usefulness of factor selection on daily returns of random subsets of S&P 100 constituents.