Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

机构投资者持股持续性对我国上市公司业绩及风险的影响研究

通过构建机构持股持续性指标体系,从四个维度全面研究机构持股持续性对上市公司业绩及风险的影响.整体来看,机构持股比例和持股稳定性的提升显著提高了公司业绩并降低了风险,反之则相反;机构持股期限与公司业绩仅呈很弱的显著负相关,导致这一异象的原因是:机构持股期限仅是一种表象,其背后隐含的是机构频繁换股的"散户化"行为及其不同的利益诉求带来了摩擦,加剧了公司经营决策和管理的不确定性,使公司业绩下降.分组研究显示:机构持股对公司业绩及风险的影响存在异质性;机构投资者只有秉持长期价值投资才利于公司业绩的提升,而以短期投机为目的的机构投资者对公司发展有害无益;机构持股稳定性越高,越有利于降低公司业绩风险;在机构持股比例高且持股期限长的公司中,机构持股比例与期限的协同效应越强,对公司业绩的改善作用越大,反之越小;在机构低比例持股的公司中,机构持股稳定性的提升对降低公司业绩风险的作用大于机构高比例持股的公司中对应的作用.     

溶解气回注提高致密油藏采收率效果及敏感性

针对致密油藏水平井产量递减快，衰竭开发采收率低等问题，提出了衰竭开发后期回注溶解气提高采收率的方法。基于新疆玛湖凹陷百口泉组地质油藏特征，建立了致密油藏多级压裂双水平井机理模型，系统研究了上述方法在致密油藏中的生产特征及敏感性。结果表明，溶解气回注可以有效提高致密油藏采收率，缓解水平井产量递减的速度。采出程度随注入量、注入速度及吞吐轮次逐渐增加；气体分子的扩散作用可增加基质的受效范围，扩大气体的作用半径；弱非均质性储层（变异系数0.2）采用溶解气吞吐提高采收率效果最佳。敏感性分析结果表明，吞吐轮次对注溶解气提高采收率的影响最大，其次是注入时间、注入速度、扩散系数、焖井时间。另外，建立的代理模型可准确预测和优化致密油藏注溶解气提高采收率效果。     

春节模型的效应期识别方法研究

春节模型的参数设定目前更多依赖于研究者的主观判断.为解决这一问题,本文首先介绍了春节因素调整的一般过程以及模型设定和检验的相关细节.在此基础上,提出一种基于"循环遍历"方式和序贯检验方法来自动选择春节模型的最优参数组合的新思路.并以社会消费品零售总额月度序列的春节模型的最优效应期长度识别为例,检验该方法的可行性和有效性.实证结果显示,基于季节峰值、离群值点、Q统计量、AICC值和BIC值等统计量的序贯检验能够有效识别出春节模型的最优效应期长度,进而改善春节模型的季节调整性能.     

四-对羟基苯基卟啉的自组装及光电特性

利用X射线光电子能谱(XPS)测试5,10,15,20-四-(对羟基苯基)卟啉单体和5,10,15,20-四-(对羟基苯基)卟啉自组装二聚体,并通过化学模拟得到卟啉自组装二聚体分子的最优构象.结果表明:两种卟啉化合物中氧原子的类型不同;在单体卟啉中未检测到瞬态表面光电压信号,卟啉自组装二聚体表现出较好的瞬态表面光伏特性;卟啉自组装二聚体的光限幅性质优于卟啉1单体.     

中国和美国信贷冲击跨境溢出效应的比较研究

本文构建了基于符号约束识别的GVAR模型,并用以考察中国和美国的信贷市场冲击对全球41个国家的不同溢出效应.研究结果表明,中国紧缩性信贷市场冲击对本国实体经济有显著的负效应,但仅限于短中期;而美国信贷冲击对本国乃至全球经济都具有相当大且持久的负影响.中国信贷冲击的跨国效应较小,且主要通过贸易渠道.中国信贷市场的本国冲击能解释中国产出近10%的波动,但无论是本国效应还是跨境传导,总需求冲击仍是驱动中国经济周期波动的最主要力量.     

基于串空间模型的认证测试方法

串空间模型认证测试方法是定理证明安全协议分析法中最具有代表性的一类.利用串空间模型理论对Needham-Schroeder(N-S)公钥协议中中间人攻击问题进行形式化分析与设计,并对其进行证明.与原有技术相比,该方法更为形式化,协议分析人员可以很方便地进行手动分析,并且更有利于协议分析自动化工具的实现.     

数控仿真中切削面计算的并行化研究

针对高离散精度模型与刀具包围体进行切削计算的情况,由于三维图像重建的计算量大,基于传统CPU实现的数控加工仿真系统不能满足实时性的三维绘制。本文利用GPU的并行性特点,提出并行方式数控仿真切削面显示算法:通过线程并行处理基于MC算法的实体构造模型中体素与刀具包围体的切削计算,根据切削后体素角点信息和边的状态查找建立的构型索引表和交点数目索引表,加速数控仿真切削面的提取,从而提高三维图像的绘制速度,满足实时性显示要求。     

一种立体织机平行打纬机构的设计与优化

针对多层织物织造过程中,各层纬纱打纬力需保持一致以及需要减少钢筘与纱线之间摩擦的要求,提出八连杆平行打纬机构,并根据工艺要求对打纬机构进行优化。以打纬机构的主轴转动140°时,筘座在后心位置处的近似静止位移Δs最小为优化目标,通过Matlab软件分析确定设计变量,优化钢筘在后死心位置的近似停留时间。运动学仿真结果表明,优化后的打纬机构不仅保证了钢筘的打纬动程和其在后死心处的静止时间,而且增大了钢筘的惯性打纬力,有利于厚重织物的打纬。     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.