银行发展对企业技术创新的作用效果检验——基于省际面板数据的分析

商业银行是我国企业技术创新过程中获取外部资金的重要渠道。利用我国各省市2000～2009年的时间序列数据,通过面板数据模型检验了银行发展对企业技术创新的作用效果,结果表明:银行发展对企业技术创新具有显著的促进作用,但其作用效果受到市场化程度等非金融因素的限制。加快金融体制的市场化改革将会更好地发挥商业银行对企业技术创新的促进作用。     

Corporate Governance,Social Responsibility,and Data Breaches

We study whether corporate governance and social responsibility are related to data breaches. We find that socially responsible companies with smaller boards and greater financial expertise are less likely to be breached. The financial impact of a breach is visible in the long term. Specifically, data‐breach firms have –3.5% one‐year buy‐and‐hold abnormal returns. Additionally, banks with breaches have significant declines in deposits and nonbanks have significant declines in sales in the long run. Finally, we find that following a data breach, companies are more likely to replace their chief executive officer and chief technology officer as well as improve their governance and social responsibility.     

基于梳状导频分布的OFDM信道估计改进算法

提出了基于梳状导频分布的OFDM信道估计的改进算法,分别在基于频域（DFT）和时 域（IDFT）的迫零内插算法的基础上运用了数据翻转的思想。该算法可以大大地减少DFT/ID FT变换时所产生的边缘效应,并且通过优化边缘位置的估计性能来提升整个OFDM信道估计的 精确度。仿真结果表明,该算法相比经典算法在性能上有较大提升。     

交通基础设施投资对经济增长推动作用研究——基于中国省级面板数据分析

本文依据生产函数模型和我国1994-2007年度30个省级地区(重庆市包含在四川省内)面板数据分析交通基础设施投资对我国各省份经济增长推动作用,得出在规模报酬不变条件下,我国各省份交通基础设施投资产出系数,进而得出经济越发达地区,交通基础设施投资对于经济增长贡献越大的结论。     

一种短距离多点无线数据采集与传输网的设计

本文以nRF905无线收发芯片和51系列单片机为硬件核心,结合目前业界应用的无线自组网技术,构筑了一个工作于ISM(Industrial Scientific Medical)频段的低功耗、高性能的无线数据通信系统。为实时性、可靠性要求较高、数据量较小的无线数据通信领域提供一个理想的解决方案。     

Population Structure and Housing Prices: Evidence from Chinese Provincial Panel Data

This study analyzes the relationship between the proportion of the economically active population aged 15–64 to total population and housing prices. A panel of 31 provinces in China from 2002 to 2014 is used in our analysis. We find empirical evidence that the impact of the population structure on housing-price growth increases as the population growth rates rise. This observation suggests that, to understand provincial housing price movements in China, one should consider the ratio of working-age population to total population in a province. The main policy implication is that Chinese policymakers need to ensure a moderated population growth to effectively promote stability in housing prices and the economy.     

Lower Bound Restrictions on Intensities in Data Envelopment Analysis

We propose an extension to the basic DEA models that guarantees that if an intensity is positive then it must be at least as large as a pre-defined lower bound. This requirement adds an integer programming constraint known within Operations Research as a Fixed-Charge (FC) type of constraint. Accordingly, we term the new model DEA_FC. The proposed model lies between the DEA models that allow units to be scaled arbitrarily low, and the Free Disposal Hull model that allows no scaling. We analyze 18 datasets from the literature to demonstrate that sufficiently low intensities—those for which the scaled Decision-Making Unit (DMU) has inputs and outputs that lie below the minimum values observed—are pervasive, and that the new model ensures fairer comparisons without sacrificing the required discriminating power. We explain why the low-intensity phenomenon exists. In sharp contrast to standard DEA models we demonstrate via examples that an inefficient DMU may play a pivotal role in determining the technology. We also propose a goal programming model that determines how deviations from the lower bounds affect efficiency, which we term the trade-off between the deviation gap and the efficiency gap.     

GMM estimation of a maximum entropy distribution with interval data

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.     

大数据在智慧城市研究与规划中的合理运用

经济实力的增长使得我国的城市发展迈入了新的发展台阶,传统的城市发展和规划方式显然难以与现代化发展方向相一致,信息技术不断发展的过程中,越来越多的城市开始投入了智慧城市建设。但智慧城市发展中,对于技术的依赖性相对较高,不论是智慧交通、智慧社区还是智慧医疗,都需要有大数据技术的支持和保障,以构建城市崭新的发展路径。基于此,本文详细分析了关于智慧城市规划中的大数据技术应用,有利于加快智慧城市发展步伐。     

一种基于Map/Reduce的并行化敌我识别信号关联方法

随着敌我识别侦察装备的广泛部署,其侦察数据呈现爆炸式增长,多源且复杂的海量信号侦察数据对后端数据分析提出了较高要求。针对传统敌我识别信号关联分析方法速度慢、效率低的问题,通过研究其关联流程的可拆分特性,提出了一种基于Map/Reduce的敌我识别信号聚类和关联分析方法,将传统敌我识别信号关联分析流程进行方位和时间拆解,在大数据平台上进行分布式并行计算,大幅提升了大数据量情况下的关联分析效率。实验结果表明,在数据量达到108条以上时,所提方法比传统方法计算效率高10倍以上。