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。     

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.     

Finite mixture of regression models for a stratified sample

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results.     

On a control chart for the Gini index with simulations

In this work we suggest the use of the Gini index on control charts. The asymptotic properties of Gini index are presented and the control charts based on appropriate confidence intervals are constructed. The suitability of the proposed charts are investigated by means of extensive simulations.