外部经济冲击对我国进出口贸易的影响——基于新兴市场经济体的研究

本文从贸易和金融渠道对我国受到其他新兴经济体的外部冲击的可能性进行了评估,并借助GVAR(Global Vector Auto-Regressive)模型方法,考虑了世界各国的交互影响来分析新兴市场国家的宏观经济波动对中国进出口产生的影响。研究发现,区域内的新兴经济市场对我国影响更为显著,韩国、印度、中国香港发生宏观经济波动时对我国进出口贸易可能造成较大的影响。基本上,中国对外贸易对韩国遭受冲击后的反应快且大,但受影响时间较短,人民币汇率波动在应对外部冲击时发挥了一定作用。中国和印度贸易合作关系大于竞争关系。     

一种基于雷达图的雷达辐射源识别算法

在分析和考察雷达辐射源(Radar Emitter)多特征模式识别算法的基础上提出了一种基于雷达图(Radar Chart)中向量夹角余弦值的分类识别算法,借助雷达图实现了雷达辐射源空间特征向量向平面向量的转换,实现对雷达辐射源型号的自动识别,给出识别置信度.通过计算机仿真得到大于85%的正确识别率,进一步验证了算法在雷达辐射源识别工程中的可用性和可行性.     

无拍频控制技术在新一代CRH380CL型高速动车组上的应用

高速动车组技术的迅猛发展对牵引变流器的性能提出了更高的要求。在动车组传动系统中,单相脉冲整流器工作时,会在中间直流环节产生2倍于电网频率的脉动分量,该脉动分量传递到逆变器输出侧会在电机上产生拍频现象,引起转矩脉动和电机过热。     

基于支持向量机的品牌特色专业评价

简述了江苏省教育厅在本科品牌专业、特色专业验收时的验收标准。利用支持向量机建立了品牌专业、特色专业验收时的验收模型。通过实例,结果表明支持向量机比较精确和有效。     

基于矢量控制的抽油机电力拖动系统仿真研究

游梁式抽油机是我国石油开采中最普遍使用的有杆泵采油地面设备之一,采取相应措施降低其能耗为多年来国内外研究的热点,近年来,矢量控制在变频调速中的应用日趋成熟,本文利用MATLAB软件成功实现了对游梁式抽油机电机拖动系统的仿真分析,对油田上最常见的三相异步电机进行了分析,搭建了矢量控制下的异步电机的数学模型,同时对抽油机的四连杆结构、三相异步电机建模,最后通过采取现场数据验证的方式证明了仿真的正确性,对今后矢量控制应用于抽油机电力拖动系统的节能改造提供了理论支持。     

一种基于支持向量机的带钢表面缺陷识别方法

鉴于支持向量机（SVM）在小样本、高维模式分类中具有的优良分类性能,可以基于多分类支持向量机来检测带铜表面的缺陷。本文构造了一类有向无环图支持向量机（DAGSVM）,利用交叉验证进行了参数和模型的选取,对冷轧带钢中几种现场易出现的缺陷进行分类,并与BP神经网络进行比较分析。实验结果表明,这类基于SVM的算法识别效率较高,较好地解决了小样本学习问题,避免了BP神经网络出现的过学习、收敛速度慢、泛化能力弱等缺点,可有效地应用于带铜表面缺陷检测。     

农村金融发展对农村经济和农民收入的影响研究——基于VEC模型的实证分析

本文基于2001—2009年的季度数据,通过构建一个向量误差修正模型,实证分析了中国农村金融发展对农村经济增长以及农民收入增加的影响。结论表明,中国农村金融发展对农民收入增加产生了显著的影响,同时中国农村金融发展、农村经济增长以及农民收入增加之间存在长期的均衡关系。     

Banks’ external financing costs and the bank lending channel: Results from a SVAR analysis

We analyze the dynamic response of banks’ financing costs to structural, macroeconomic shocks, which we identify by imposing combinations of zero and sign restrictions on impulse responses. For the estimation we combine US bank balance sheet data from the Call reports with macroeconomic aggregates over the period from 1984Q1 to 2007Q3. We find that banks’ financing costs mainly respond to monetary policy and aggregate demand shocks. Furthermore, funding costs of undercapitalized and illiquid banks increase more strongly after a contractionary monetary policy shock as compared to better capitalized and more liquid banks. These results provide support for the view that banks’ financing costs represent an important element of the bank lending channel.     

STRUCTURAL VECTOR AUTOREGRESSIONS: CHECKING IDENTIFYING LONG‐RUN RESTRICTIONS VIA HETEROSKEDASTICITY

Long‐run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just‐identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have been used for this purpose. Three main approaches have been used, exogenously generated changes in the unconditional residual covariance matrix, changing volatility modelled by a Markov switching mechanism and multivariate generalized autoregressive conditional heteroskedasticity models. Using changes in volatility for checking long‐run identifying restrictions in structural VAR analysis is illustrated by reconsidering models for identifying fundamental components of stock prices.     

Disappearing Dividends: Implications for the Dividend–Price Ratio and Return Predictability

The conventional dividend–price ratio is highly persistent, and the literature reports mixed evidence on its role in predicting stock returns. We argue that the decreasing number of firms with a traditional dividend‐payout policy is responsible for these results, and develop a model in which the long‐run relationship between the dividends and stock price is time varying. An adjusted dividend–price ratio that accounts for the time‐varying long‐run relationship is considerably less persistent. Furthermore, the predictive regression model that employs the adjusted dividend–price ratio as a regressor outperforms the random‐walk model. These results are robust with respect to the firm size.