The effect of media sexism on women’s political ambition: evidence from a worldwide study

This paper presents results from one of the first global studies on the relationship between media sexism and the share of candidates for the lower chamber of national parliaments who are women. Data on media sexism come from the Global Media Monitoring Project, the most reliable worldwide source for media coverage from a gender perspective. The data on share of female political candidates come from the Inter-Parliamentary Union. The results show that that there is, even when controlling for the number of women in parliament, electoral system, gender quotas, level of human development, level of women’s rights, freedom from corruption, and media access, a significant relationship between media sexism—measured as (i) the share of all news subjects that are women and (ii) the share of all news subjects portrayed in the function of experts who are women—and the share of women candidates for parliament: the higher the level of media sexism, the lower the share of women candidates. The theory discussed zooms in on a bystander effect: We hypothesize that sexist portrayals of women in the media stifle ambition among women who, in a less sexist media environment, would be willing to stand as political candidates.     

中国对俄直接投资对中俄贸易的影响

中国对俄直接投资的迅速发展必然会对中俄贸易产生一定的影响.为深入分析中国对俄直接投资的贸易效应,采用面板数据模型,对中国对俄直接投资与进出口之间的关系进行实证研究,并将俄罗斯与中国其他主要投资东道国的情况进行比较分析.结果表明:中国对俄直接投资具有显著的出口创造效应与进口创造效应,且出口创造效应高于进口创造效应;与其他主要投资东道国相比,中国对俄直接投资对出口的促进作用较强,而对进口的促进作用较弱.     

FDI对中国经济增长的区域效应和动态效应

以我国各个省市作为横截面单元,利用各横截面单元1988~2005年的样本组成面板数据,进而建立动态面板数据模型来考察FDI对我国不同地区经济增长的影响。结果表明,FDI对我国经济增长具有促进作用,但是滞后2期FDI对我国经济增长具有负面影响。同时,FDI对不同地区的影响有显著差异,对东部地区而言,FDI对经济增长的效应最为明显。     

社会保障支出、经济增长与居民消费的区域差异研究

基于扩展的杜森贝利消费函数,以区域差异的视角对东、中、西部地区1997～2009年的社会保障、经济增长与居民消费之间的长短期效应进行实证研究。结果显示,我国社会保障支出、经济增长与居民消费存在长期的协整关系,居民消费水平与社会保障支出呈显著正相关,西部地区社会保障支出对居民消费的拉动作用最大,中部次之,东部最小。其政策含义是:应加大中西部地区在社会保障领域的支持力度,促进地区间公共服务均等化,降低人们的不确定性预期,实现居民消费的稳定性和经济健康发展。     

Rate efficient estimation of realized Laplace transform of volatility with microstructure noise

In this paper, we consider the problem of estimating the Laplace transform of volatility within a fixed time interval [0,T] using high‐frequency sampling, where we assume that the discretized observations of the latent process are contaminated by microstructure noise. We use the pre‐averaging approach to deal with the effect of microstructure noise. Under the high‐frequency scenario, we obtain a consistent estimator whose convergence rate is , which is known as the optimal convergence rate of the estimation of integrated volatility functionals under the presence of microstructure noise. The related central limit theorem is established. The simulation studies justify the finite‐sample performance of the proposed estimator.     

农村居民消费对我国经济增长影响分析

本文通过建立面板数据模型测度了农村居民消费对我国经济增长的影响效应,认为农村居民消费对我国经济增长具有正向促进作用但不够明显,分析了扩大农村居民消费需求促进经济增长所面临的障碍因素并提出了相应的对策建议。     

Zero-inflated models for adjusting varying exposures: a cautionary note on the pitfalls of using offset

Zero-inflated count data are frequently encountered in public health and epidemiology research. Two-parts model is often used to model the excessive zeros, which are a mixture of two components: a point mass at zero and a count distribution, such as a Poisson distribution. When the rate of events per unit exposure is of interest, offset is commonly used to account for the varying extent of exposure, which is essentially a predictor whose regression coefficient is fixed at one. Such an assumption of exposure effect is, however, quite restrictive for many practical problems. Further, for zero-inflated models, offset is often only included in the count component of the model. However, the probability of excessive zero component could also be affected by the amount of ‘exposure’. We, therefore, proposed incorporating the varying exposure as a covariate rather than an offset term in both the probability of excessive zeros and conditional counts components of the zero-inflated model. A real example is used to illustrate the usage of the proposed methods, and simulation studies are conducted to assess the performance of the proposed methods for a broad variety of situations.     

Binary Dynamic Logit for Correlated Ordinal: estimation,application and simulation

We evaluate the estimation performance of the Binary Dynamic Logit model for correlated ordinal variables (BDLCO model), and compare it to GEE and Ordinal Logistic Regression performance in terms of bias and Mean Absolute Percentage Error (MAPE) via Monte Carlo simulation. Our results indicate that when the proportional-odds assumption does not hold, the proposed BDLCO method is superior to existing models in estimating correlated ordinal data. Moreover, this method is flexible in terms of modeling dependence and allows unequal slopes for each category, and can be used to estimate an apple bloom data set where the proportional-odds assumption is violated. We also provide a function in R to implement BDLCO.     

A multivariate zero-inflated binomial model for the analysis of correlated proportional data

In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.