FDI 对安徽省装备制造业发展的影响分析

外商直接投资（FDI）在拓宽国内资金需求渠道的同时,具有较强的技术溢出效应,迎合了装备制造业资本和技术密集的双重特征。当前,安徽省装备制造业处在资本密集型占主导的发展阶段,对 FDI的偏好性较强。研究表明,安徽省装备制造业与FDI间具有较强的相关性,FDI能够有效促进安徽省装备制造业发展,但安徽省装备制造业对FDI的吸引力较弱,且各具体行业对 FDI依赖程度存在较大差异。据此提出相应的建议,一方面,安徽省应注重装备制造业转型升级,提升行业整体竞争力;另一方面,要优化投资环境,增强安徽省装备制造业对FDI的引致能力。     

社会资本对居民生活垃圾分类行为的影响机理分析

社会资本与环境保护合作行为的逻辑关系已经成为环境管理的一个重要研究方向。基于219户城镇居民调查数据的实证研究发现,除了年龄、受教育年限和中共党员身份外,以社会网络、社会规范和社会信任为要素的社会资本,对提高居民生活垃圾分类水平有显著的正向影响。具体而言,社会网络能够降低居民机会主义和“搭便车”的行为倾向;社会规范能够提高居民行为的可预测性,增强居民投资环境保护集体行动的信心;社会信任则通过降低交易成本,促进居民生活垃圾分类的自主合作行为。为从源头上化解我国生态环境管理面临的“垃圾围城”困境,政府应积极促进居民间的网络互动,完善互惠共享的社会规范,提升居民间的普遍信任,同时加大对生活垃圾分类知识和技巧的宣传力度、发挥党员在遵守社会规范中的带头作用,通过社会资本发展促进公众的自主环境保护合作行为。     

论数+形+量结构中量词的特点

现代汉语中数量词中插入形容词的结构即数+形+量结构比较常见,例如,一大张纸,一大群人,一长根铁丝。陆俭明先 生对这种语言现象进行了详细的描写,但是还是留下了一些问题给大家讨论。例如,为什么数量词中间可以插入形容词,插入 形容词对整个结构有什么影响。从量词的特点和形容词的语义指向来看,数+形+量结构中的量词具有空间性、物质性、描写 性的特点,使得形容词能够进入该结构,形容词的语义指向不同,使整个结构有不同的表达功能。     

港口物流与区域产业结构优化--基于湛江市的实证

基于2004-2014年的数据,采用灰色关联度和线性回归模型对湛江市港口物流与区域产业结构优化效应进行分析。结果显示：港口物流两大指标与湛江市 GDP 及三大产业的平均关联度均超过0.7,回归分析亦得到同样的结果,说明发展港口物流对区域经济增长和产业结构优化有重要作用。优化湛江市产业结构,可以从做好临港产业发展规划,引导钢铁和石化产业集聚;完善港口物流基础设施,大力发展集装箱运输;积极推进内陆无水港建设,拓宽内陆经济腹地;大力推进"保税物流中心"建设,推动区港联动发展等几个方面进行。     

Testing Serial Correlation in Partially Linear Additive Errors-in-variables Models

This article considers testing serial correlation in partially linear additive errors-in-variables model. Based on the empirical likelihood based approach, a test statistic was proposed, and it was shown to follow asymptotically a chi-square distribution under the null hypothesis of no serial correlation. Finally, some simulation studies are conducted to illustrate the performance of the proposed method.     

Intraday Serial Correlation,Volatility, and Jump: Evidence from China's Stock Market

In this article, we investigate the relationships among intraday serial correlation, jump-robust volatility, positive and negative jumps based on Shanghai composite index high frequency data. We implement variance ratio test to quantify intraday serial correlation. We also measure the continuous part of realized volatility using jump-robust MedRV estimator and disentangle positive and negative jumps using Realized Downside Risk Measure and Realized Upside Potential Measure proposed by Bi et al., (2013 Bi, T., Zhang, B., Wu, H. (2013). Measuring downside risk using high frequency data–realized downside risk measure. Communications in Statistics–Simulation and Computation 42(4):741–754.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). We find that intraday serial correlation are positively correlated with jump-robust volatility and negatively correlated with negative jumps which confirm the LeBaron effect.     

Visualization for Large‐scale Gaussian Updates

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large‐scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large‐scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

空间面板杜宾模型的Bootstrap Wald-COMFAC检验研究

将共同因子约束(COMFAC)的Wald检验问题引入到空间面板模型中,讨论空间面板杜宾模型与空间面板误差模型的识别问题。蒙特卡洛模拟表明:在有限样本下,基于渐近临界值的Wald检验有着良好的检验功效,但存在着较为严重的尺度扭曲。进一步采用残差Bootstrap方法,在不损失检验功效的前提下,能够显著地降低检验的尺度扭曲。因此,残差Bootstrap方法是更为有效的检验方法。