基于SCD模型的金融超高频数据信息含量研究

针对随机波动条件持续期模型(SCD模型)对于金融超高频数据的模拟优势,采用股票交易的分笔数据,在模型中引入消息指标变量、交易量和买卖价差三类微观结构变量,通过分析价格持续期与这些结构变量之间的相关性,以研究价格形成过程中的信息传递过程。研究结果表明:利好消息通常导致交易强度增加,利空消息通常会导致交易持续期延长,价格变化对交易持续期造成的影响有限;小盘股买卖价差影响后续价格交易持续期,但大盘股的影响并不明显;交易量的增加具有信息含量。     

我国制造业产能利用率测度与影响因素研究——基于十个重点行业

选取制造业的十个重点行业,使用2001-2014年的面板数据,运用数据包络分析法(DEA)对其产能利用率、技术效率和设备利用率进行了测算,结果显示:我国制造业的部分行业存在严重的产能过剩问题;非金属矿物制品业、黑色金属冶炼和压延加工业等传统行业的产能利用率较低;通信设备、计算机及其他电子设备制造业等新兴行业的产能利用率相对较高.剖析制造业产能过剩的影响因素,发现市场需求与产能利用率存在显著的正相关关系,资本密集度、就业水平、开放度、金融支持与产能利用率有显著的负相关关系.     

当前大学生对国学教育的认知和需求——以某高校调查数据为例

对大学生国学认知程度和需求的调查是有效开展国学教育的基础。这个基础必须通过详实的数据调查和实证分析来奠定。通过在某高校展开问卷调查,我们发现大学生对于国学的不甚了解是一个普遍现象。高校学生对国学教育的需求不仅存在,而且是理性而合理的。真正影响高校国学教育效果的症结仍然是师资力量、课程内容、教学步骤等问题。我们建议应该遵循由易到难、从日常习用到弘文奥义、分类教学的原则,系统地做好国学教育内容的顶层设计。     

大数据和企业精准营销相关性分析

对大数据和企业精准营销相关性的分析能够为精准营销的实施提供基础和技术平台,为企业对大数据的管理提供一定的方向,将很大程度影响企业的竞争力和发展。在拟定大数据和精准营销指标体系基础上,通过对黑龙江省服务业、商业企业、制造业和现代农业企业进行问卷调查,运用结构方程模型分析其相关性。分析结果显示,大数据与精准营销具有显著相关性,客户数据挖掘对大数据具有显著影响,精准广告投放对精准营销具有显著影响。据此指出,企业应当充分利用大数据技术,注重客户数据挖掘,提高营销的精准性,使其能适应市场竞争带来的机遇与挑战。     

Multiscale Combined Forecast of Carbon PriceBased on Mixed Structure Data

Accurate forecast of the carbon trading price is of great significance in promoting the scientific and rational development of carbon trading market. Therefore, this paper proposes a multi-scale combined forecasting method for carbon price based on mixed structure data. First, the Google Index is used to extract the unstructured data related to the carbon price.The dimensions of unstructured data are reduced based on principal component analysis. Then, EMD is employedto the structured data,unstructured data and the carbon trading price to obtain different IMFs, which are reconstructed by the Fine-to-Coarse technique to get low, high frequency sequence and trend sequence. Furthermore, the three items are predicted respectively by using ARIMA, PLS and neural networks according to the features of each scale in time series. Finally, the forecasting results are summed to get the carbon price forecast sequence. The proposed method is used to forecast carbon price in EU. The empirical results show that the prediction accuracy of the model is higher than that of the single prediction method and the prediction method that time series aren’t decomposed by EMD, which is of great applicability.     

大数据时代国防动员潜力调查之路

如何应用大数据提升国防动员潜力调查的效率是当前政府重点关注的话题。从国防动员潜力大数据的定义和特征着手,分析大数据下国防动员潜力调查所具备的优势,发现仍然存在思维局限、指标不统一、体制不健全、技术缺乏及数据共享与防护难协同等困境。通过思维意识、管理体制、指标体系、核心技术、专业人才、安全共享等六大模块,打破“数据不可用、数据不会用”的现实瓶颈,实现整合国防动员数据资源、延展数据内容和提高工作效率的目标。     

大学化学实验课程中有效数字的记录

有效数字是大学化学实验课程的主要教学内容之一,是培养学生在课程中理解并在日常学习与工作中规范使用有效数字,提升其对有效数字与测定误差之间关系的认识水平,树立良好规范的数字记录习惯,具有十分重要的现实意义。论述介绍了目前大学化学实验教学中关于有效数字方面存在的主要问题,并采用相应的举措建立和完善了教学方案以利于更好地解决存在的问题。通过几个学期的教学实践发现新教学方案取得了初步的成效,同时严格按照有效数字的要求记录与计算数据也是培养学生具有严谨科学素养的一条重要途径。     

Spatial Data Usage,Spatial Thinking and Spatial Knowledge Generation: The Case of Planning Practitioners in Arequipa,Peru

Spatial thinking and spatial knowledge generation in decision-making are still not mature fields of study in planning research, despite these being crucial elements in addressing the issues of the twenty-first-century city. This article contributes to their understanding by exploring their interrelationships with spatial data usage. Through storylines, it analyzes the arguments that planning practitioners offer in support of infrastructure-led decisions in Arequipa (Peru), before and after spatial data usage. The article concludes that spatial data usage improve spatial thinking to different extents, yet suggests aligning spatial data generation and the inclusion of GIS-based spatial analyses with the spatial knowledge needed by routine planning practice.     

Simultaneous Confidence Intervals for the Population Cell Means,for Two-by-Two Factorial Data,that Utilize Uncertain Prior Information

Consider a two-by-two factorial experiment with more than one replicate. Suppose that we have uncertain prior information that the two-factor interaction is zero. We describe new simultaneous frequentist confidence intervals for the four population cell means, with simultaneous confidence coefficient 1 ? α, that utilize this prior information in the following sense. These simultaneous confidence intervals define a cube with expected volume that (a) is relatively small when the two-factor interaction is zero and (b) has maximum value that is not too large. Also, these intervals coincide with the standard simultaneous confidence intervals obtained by Tukey’s method, with simultaneous confidence coefficient 1 ? α, when the data strongly contradict the prior information that the two-factor interaction is zero. We illustrate the application of these new simultaneous confidence intervals to a real data set.     

Nonparametric analysis of treatment effects with missing observations

This paper develops a test for comparing treatment effects when observations are missing at random for repeated measures data on independent subjects. It is assumed that missingness at any occasion follows a Bernoulli distribution. It is shown that the distribution of the vector of linear rank statistics depends on the unknown parameters of the probability law that governs missingness, which is absent in the existing conditional methods employing rank statistics. This dependence is through the variance–covariance matrix of the vector of linear ranks. The test statistic is a quadratic form in the linear rank statistics when the variance–covariance matrix is estimated. The limiting distribution of the test statistic is derived under the null hypothesis. Several methods of estimating the unknown components of the variance–covariance matrix are considered. The estimate that produces stable empirical Type I error rate while maintaining the highest power among the competing tests is recommended for implementation in practice. Simulation studies are also presented to show the advantage of the proposed test over other rank-based tests that do not account for the randomness in the missing data pattern. Our method is shown to have the highest power while also maintaining near-nominal Type I error rates. Our results clearly illustrate that even for an ignorable missingness mechanism, the randomness in the pattern of missingness cannot be ignored. A real data example is presented to highlight the effectiveness of the proposed method.