结构向量自回归模型因果图的信息论辨识方法

由观测数据确定变量间的因果关系是时间序列分析的重要内容.本文利用图模型方法研究结构向量自回归模型变量间的因果关系,通过时间序列因果图的建立将问题转化为时间序列因果图结构的辨识.基于信息论方法提出了因果性定向的三步准则,利用关联积分估计互信息和条件互信息.模拟结果显示本方法能更有效地辨识结构向量自回归模型因果图的因果结构.     

非线性结构向量自回归模型因果关系的图模型辨识方法（英文）

确定变量间的因果关系是时间序列分析的重要内容.传统的图模型因果推断算法有着明显的局限性,要求模型是线性的且噪声项服从Gauss分布.本文利用图模型方法辨识非线性结构向量自回归模型变量间的因果关系,给出了一种基于互信息和条件互信息的非线性结构向量自回归因果图模型结构的非参数辨识方法.数值模拟结果验证了方法的有效性.     

利用ICP-OES测定纺织品中总铅总镉含量的不确定度的评估

为了评估利用ICP-OES测定纺织品中总铅总镉含量的不确定度,本文参考CNAS-GL06:2006和GB/T30157:2013,建立了数学模型,并根据测试过程中产生不确定度的变量建立了因果图,通过转化数学模型和因果图,着重对测试过程中的中间精密度和正确度等因素进行试验,得到利用ICP-OES测定纺织品中总铅总镉含量的不确定度分别为11%和6.2%。     

时滞数据动态过程传感器故障诊断的改进方法

提出利用不断更新的时滞数据矩阵建立变化的动态主元模型对某些动态系统内的传感器故障进行检测，利用变量贡献图的样本平均对故障进行识别的方法．该方法克服了传统静态主元分析方法不适于分析具有动态特性的系统传感器故障的缺陷，而且改善了常用的基于时滞数据建立的动态主元分析法对于分析变量间关系不确定的动态系统存在的局限性．最后给出了3种方法的实验比照结果，验证了方法的有效性，     

基于改进因果图法的CBTC系统车载功能测试方案研究

针对CBTC系统的车载设备的测试方法和具体的测试案例进行研究,选取以因果图法为主体的改进方法对测试进行指导工作,重点研究因果图法中由因果图转化为判定表的条件遍历算法,并选取车载ATP超速防护的功能为例研究改进后的因果图法在车载设备系统测试中的具体应用.实验结果表明:该文提出的测试方案切实有效,可以进行推广.     

基于动量动力学模型的行人关系分析

进行了行人行为分析及行人关系研究。考虑到在机器视觉领域大多数研究仅关注于目标行为分类与识别,而目标间因果关系判别研究较少,且现有理论停留在心理学领域中的因果关系表达,提出了一种基于动量动力学模型的目标间因果关系识别方法,用于实现行人间因果关系的识别及量化计算。该方法利用Cam shift算法获得目标在视频中的位置并依据因果概念,构建动量动力模型,然后基于动量动力模型进行因果关系判别,最后计算因果值。根据因果值范围识别出视频行人间的三种因果关系:导致、促进和阻碍。实验表明,上述方法可以在视频监控条件下识别两运动行人间的因果关系。     

具有协变量的高斯图模型的结构学习

图模型是一种研究变量之间相依关系的重要工具．除了节点变量外,数据常常包括协变量而且可能影响网络结构．然而现有关于图模型的工作大多仅考虑节点变量．本文基于图模型研究具有协变量的网络结构特征学习问题,在稀疏正则化的框架下,通过假设变量之间的条件独立为线性关系,建立具有协变量信息的稀疏高斯图模型,估计网络结构特征．所得结果具有实际解释性且易于求解,我们利用坐标下降法求解模型,通过实验说明含协变量比无协变量的效果更好,从而说明本文模型的高效性和实用性．     

制造过程中质量控制系统的因果追溯性研究

针对制造过程质量控制系统可追溯性存在的问题,在质量控制系统中引入因果追溯性的概念,结合离散事件分析系统——DEVS的特点研究了基于DEVS的制造过程质量控制系统及其因果追溯性;按照T-DEVS的描述及其基本规则构建了制造过程的质量控制系统,并就此系统的因果追溯性进行阐述和定义.论文提供一种建立因果追溯树的方法,进而实现制造过程质量控制系统的因果追溯.     

应用PDCA技术优化改进提高线纹尺检定效率和准确度

文章利用质量管理中的PDCA技术对所使用的线纹尺检定装置进行了从人员、环境、设备、方法几个方面的分析,提出了实施对策表和因果图,通过实验室成员的共同努力,有效地提高了检定线纹尺的准确度和效率.     

MEWMA控制图失控信号的诊断

MEWMA控制图是最有应用价值的多元控制图之一.然而,当MEWMA图发出失控信号时,很难说明哪个变量或哪些变量处于失控状态.笔者利用主成分分析进行MEWMA控制图失控信号的诊断,并引用Lowry等人提出的实例进行了实证分析.     

Functional Causality between Oil Prices and GDP Based on Big Data

This paper examines the causal relationship between oil prices and the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia. The study is carried out by a data set collected quarterly, by Saudi Arabian Monetary Authority, over a period from 1974 to 2016. We seek how a change in real crude oil price affects the GDP of KSA. Based on a new technique, we treat this data in its continuous path. Precisely, we analyze the causality between these two variables, i.e., oil prices and GDP, by using their yearly curves observed in the four quarters of each year. We discuss the causality in the sense of Granger, which requires the stationarity of the data. Thus, in the first Step, we test the stationarity by using the Monte Carlo test of a functional time series stationarity. Our main goal is treated in the second step, where we use the functional causality idea to model the co-variability between these variables. We show that the two series are not integrated; there is one causality between these two variables. All the statistical analyzes were performed using R software.     

The innovation- growth link in OECD countries: Could other macroeconomic variables matter?

This study investigates the Granger causal relationships between innovation, economic growth, information and communication technology (ICT) infrastructure, government consumption expenditure, gross capital formation, foreign direct investment, and trade openness. Using panel data from 32 high-income OECD countries from 1970 to 2016 and panel cointegration techniques, results show that these variables are cointegrated. The Granger causality tests further confirm that, taking other variables into account, there is bi-directional causality between innovation and economic growth in the long run. Moreover, both economic growth and innovation are generally impacted in the long run by the other variables that we consider. The short-run causality results reveal a diverse pattern of short-run adjustment dynamics among the variables including the possibility of feedback among some of them. Important policy implications for sustainable economic growth and higher innovation suggest elevating government consumption expenditure, increasing capital formation, further opening of countries' economies to trade, as well as improving ICT infrastructure.     

Time-varying causality between research output and economic growth in US

This main purpose of this paper is to investigate the causal relationship between knowledge (research output) and economic growth in US over 1981–2011. To overcome the issues of ignoring possible instability and hence, falsely assuming a constant relationship through the years, we use bootstrapped Granger non-causality tests with fixed-size rolling-window to analyze time-varying causal links between two series. Instead of just performing causality tests on the full sample which assumes a single causality relationship, we also perform Granger causality tests on the rolling sub-samples with a fixed-window size. Unlike the full-sample Granger causality test, this method allows us to capture any structural shifts in the model, as well as, the evolution of causal relationships between sub-periods, with the bootstrapping approach controlling for small-sample bias. Full-sample bootstrap causality tests reveal no causal relationship between research and growth in the US. Further, parameter stability tests indicate that there were structural shifts in the relationship, and hence, we cannot entirely rely on full-sample results. The bootstrap rolling-window causality tests show that during the sub-periods of 2003–2005 and 2009, GDP Granger caused research output; while in 2010, the causality ran in the opposite direction. Using a two-state regime switching vector smooth autoregressive model, we find unidirectional Granger causality from research output to GDP in the full sample.     

The causal relationship between renewable energy consumption and economic growth: evidence from Europe

The European Union Renewable Energy Directive sets an objective of increasing the renewable energy share of the used renewable energy in the EU by 2020. The objective of this study is to analyze and compare the short-run and long-run relationship between renewable energy consumption and economic growth in 12 European Union countries and to derive implications for renewable energy policy. To do so, we apply panel vector error correction model using the available annual data from 1990 to 2014 on 12 European Union countries. Moreover, Granger causality test is conducted to examine whether there exists any causal linkage between economic growth and renewable energy consumption. The findings indicate the presence of unidirectional causality running from economic growth to renewable energy consumption in the short run. However, in the long run, a bidirectional causal relationship between the variables in question exists.     

我国科技进步对国家标准发展的影响研究

本文以我国1990年～2009年数据为基础,采用协整和因果检验的方法,实证分析了我国的科技进步、研发创新与国家标准之间的关系。结果表明:研发与标准确为因果关系,且加强研发活动对促进国家标准是有积极意义的,其对国家标准的贡献为0.24。     

我国科技进步促进地方标准发展的实证研究

本文从技术的角度,研究我国地方标准发展的动力和原因,具体以1990~2009年数据为基础,采用协整和因果检验的方法,实证分析了技术进步对地方标准发展的数量关系。研究结果表明:技术进步对地方标准的发展具有根本性的影响,地方标准是技术发展的结果,增加技术活动的强度会对地方标准数量的增长产生影响。仅考虑单个解释变量,则研发经费的增加对地方标准增长的影响是0.49,如果在双解释变量模型中,专利申请对地方标准的影响已上升到2.9。并且,由于地方标准具有"知识溢出"的特征,研发主体更加愿意"搭便车",更加愿意把成果变为专利,以保护其知识产权。所以,在双变量的模型中,地方标准的变化与专利申请成正向关系,与研发资金成负向关系。这也是符合标准化发展一般规律的。     

Research on the Relationship Between Garlic and Young Garlic Shoot Based on Big Data

In view of the problems such as frequent fluctuation of garlic price, lack of efficient forecasting means and difficulty in realizing the steady development of garlic industry, combined with the current situation of garlic industry and the collected data information. Taking Big Data platform of garlic industry chain as the core, using the methods of correlation analysis, smoothness test, co-integration test, and Granger causality test, this paper analyzes the correlation, dynamic, and causality between garlic price and young garlic shoot price. According to the current situation of garlic industry, the garlic industry service based on Big Data is put forward. It is concluded that there is a positive correlation between garlic price and young garlic shoot price, and there is a long-term stable dynamic equilibrium relationship between young garlic shoot price and garlic price fluctuation, and young garlic shoot price can affect garlic price. Finally, it is proposed to strengthen the infrastructure construction of garlic Big Data, increase the technological innovation and application of garlic Big Data technology, and promote the safety and security ability of the whole industry to promote the development of garlic industry.     

专利产出与区域经济增长的动态关联机制分析

运用动态计量经济学的分布滞后模型、单位根检验模型、协整分析模型和Granger因果检验模型,以及专利产出与经济增长数据,对首都区域专利产出与经济增长关联机制进行了动态计量经济实证分析.结果显示:首都地区专利产出与经济增长当前及之后具有两期的关联性,平均来讲,专利产出每增加1%,首都区域经济当年将增长0.4923%,专利产出在3年内共使得经济增长了0.9847%;专利产出增长与GDP增长存在长期稳定的动态均衡关系,但是其对首都地区经济增长促进作用还不够;专利产出是经济增长Granger意义上的原因,但专利产出没有有效地促进首都区域经济增长,二者之间没有形成一种协调互动的反馈机制.     

Gene regulatory network discovery using pairwise Granger causality

Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time‐series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pairwisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full‐model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell‐cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.Inspec keywords: biology computing, cancer, causality, cellular biophysics, genetics, genomics, time seriesOther keywords: gene regulatory network discovery, pairwise Granger causality, gene expression data, drug development, time‐series data, synthetic data, spurious causalities, full‐model Granger causality detection, vector autoregressive model, real human HeLa cell‐cycle dataset, Akaike information criterion, degree distributions, network hubs     

The role of information and communication technology in encountering environmental degradation: Proposing an SDG framework for the BRICS countries

Sustainability through information and communication technologies is a complex matter, raising interesting debate among researchers. Pursuing the same, this research investigates the impact of information and communication technologies, economic growth, and financial development on carbon dioxide emissions by simultaneously testing the Environmental Kuznets curve (EKC) hypothesis in BRICS countries. In doing so, this study employs Methods of Moments - Quantile Regression, which confirms that the effects of the explanatory variables vary across different quantiles of carbon dioxide emissions. The overall results indicate that economic growth and financial development contribute to carbon dioxide emissions across all quantiles, while information and communication technologies significantly mitigate the level of carbon dioxide emissions only at lower emissions quantiles. Moreover, the results confirm the presence of the EKC hypothesis. Interestingly, the effect of economic growth and information and communication technologies on carbon dioxide emissions is lowest in magnitude at lower quantiles and highest at higher quantiles of carbon dioxide emissions. The empirical findings of DH panel heterogenous causality test confirm bidirectional causality between the model parameters, indicating that any policy intervention concerning explanatory variables significantly causes carbon dioxide emissions and vice versa. The results set out the foundation for policymakers to devise a policy framework to attain the objectives of Sustainable Development Goals (SDGs).