CO2 emissions,GDP and energy intensity: A multivariate cointegration and causality analysis for Greece, 1977–2007

This paper deals with the causal relationship analysis between Gross Domestic Product, Energy Intensity and CO₂ emissions in Greece from 1977 to 2007, by means of Johansen cointegration tests and Granger-causality tests based on a multivariate Vector Error Correction Modeling. Results indicate that there is a set of uni-directional and bi-directional causalities among the selected time series. We performed a model Variance Decomposition Analysis using Choleski technique and we provided a comparison with other studies. The findings of the study have significant policy implications for countries like Greece as the decoupling of CO₂ emissions and economic growth seems quite unlikely.     

Introduction to the special section on Campbell’s and Rubin’s conceptualizations of causality.

Causality plays a fundamental role in scientific explanation. This introduction describes 2 target articles and 3 commentaries on 2 influential perspectives on causal inference, one developed by Donald Campbell and the other developed by Donald Rubin. One goal of this special section is to introduce Rubin’s causal model to psychologists who may be largely unfamiliar with it. Another goal is to compare Rubin’s conceptualization with Campbell’s perspective, to enrich readers’ understanding of both views. All of the authors of this special section perceive many similarities between the 2 approaches. Even so, by comparing and contrasting the 2 perspectives, the authors also believe that it is possible to strengthen both approaches. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Study on the Impact of Industrial Production Index (IPI) to Beverage,Food and Tobacco Sector Index with Special Reference to Colombo Stock Exchange

This study attempts to explore the impact of Industrial Production Index to sector performance of Beverage, Food and Tobacco in Colombo Stock Exchange Sri Lanka. Secondary data were used for the analysis. Sector index was taken from the Data Library of Colombo Stock Exchange and Industrial Production Index was taken from Annual Reports of Central Bank Sri Lanka. Monthly data were gathered from January 2002 to December 2014. For the time series data set, first, the stationary was checked using Augmented Dickey-Fuller and Phillips Perron Tests of E-views software. The results of stationary check show that the sector index is stationary at 1^st difference in both ADF and PP tests. Industrial Production Index is stationary at 2^nd difference in ADF test but 1^st difference is stationary in PP test. Because of the seasonal trend in IPI 12^th difference also considered and it is stationary in both ADF and PP tests. Granger Causality test was used to find out the causal relationship between variables. Results show the one way causality that the changes occurring in IPI will have an effect on changes in sector index. But changes in sector index will not have any effect on changes in IPI. Finally a regression was used to find out the relationship between variables. A Pearson Correlation coefficient was checked to find the correlation among variables before moving to the regression. Result of correlation test shows 84% higher correlation between variables and regression result shows a significant positive relationship amongvariables. The study concludes that Industrial Production Index will positively impact on Beverage, Food and Tobacco sector Index in Sri Lanka. It can be recommended that the changes occurring in the IPI be considered by Investors when they buy and sell stocks in BFT sector.     

On the Expressiveness of Higher Dimensional Automata: (Extended Abstract)

In this paper I compare the expressive power of several models of concurrency based on their ability to represent causal dependence. To this end, I translate these models, in behaviour preserving ways, into the model of higher dimensional automata, which is the most expressive model under investigation. In particular, I propose four different translations of Petri nets, corresponding to the four different computational interpretations of nets found in the literature.I also extend various equivalence relations for concurrent systems to higher dimensional automata. These include the history preserving bisimulation, which is the coarsest equivalence that fully respects branching time, causality and their interplay, as well as the ST-bisimulation, a branching time respecting equivalence that takes causality into account to the extent that it is expressible by actions overlapping in time. Through their embeddings in higher dimensional automata, it is now well-defined whether members of different models of concurrency are equivalent.     

A preferential semantics for causal reasoning about action

One of the principal concerns in the research area of Reasoning about Action is determining the ramifications of actions in changing environments. A particular tendency emerging in recent literature endorses the explicit incorporation of causal knowledge in logic-based action theories. It is argued that causal extensions not only enhance the expressive power of theories of action, but may also provide more concise and intuitive representations. This paper investigates semantics for causal reasoning about action and change. It does so by exploring the role of several fundamental underlying principles, such as the Principle of Minimal Change and the Principle of Causal Change. This work culminates in a general unifying semantics for a class of action theories represented by a number of recent and influential approaches – in particular, the causal relationship approach of Thielscher and the causal systems with fixed-points suggested by McCain and Turner. The unifying augmented preferential semantics, emerging as a result of this study, captures both Principles of Change and shows their clear and distinct roles.     

Complexity results for structure-based causality

We give a precise picture of the computational complexity of causal relationships in Pearl's structural models, where we focus on causality between variables, event causality, and probabilistic causality. As for causality between variables, we consider the notions of causal irrelevance, cause, cause in a context, direct cause, and indirect cause. As for event causality, we analyze the complexity of the notions of necessary and possible cause, and of the sophisticated notions of weak and actual cause by Halpern and Pearl. In the course of this, we also prove an open conjecture by Halpern and Pearl, and establish other semantic results. We then analyze the complexity of the probabilistic notions of probabilistic causal irrelevance, likely causes of events, and occurrences of events despite other events. Moreover, we consider decision and optimization problems involving counterfactual formulas. To our knowledge, no complexity aspects of causal relationships in the structural-model approach have been considered so far, and our results shed light on this issue.     

对区域货币效率的Granger检验与自回归分析

Granger检验结果表明,西北和全国贷款规模与经济增长均存在双向因果关系,但西北地区贷款规模对经济增长影响的置信度较低。由自回归发现西北贷款规模对经济增长的影响因子是0.294935,全国为0.429096,进一步证实西北地区贷款规模对经济增长的影响显著地弱于全国。检验分析还表明西北地区贷款规模对GDP的作用时滞较长。西北地区货币政策低效的直接原因为投资对经济增长拉动力度弱,深层次背景在于政府投资占比大,内向型区域经济特征等因素使西北地区投资收益率低。     

运用偏相关因果分析推断神经网络的真实结构

自从格兰杰提出因果关系的概念之后,格兰杰因果关系在构造神经网络的结构方面的应用越来越广泛,因为它可以得到神经网络的一个有向图。对于只有两个神经元的神经元网络,可以用通常的格兰杰因果关系去分析它们谁是因,谁是果。对于三个神经元以上的神经网络,由于神经元之间存在间接的作用,就不能象对两个神经元直接运用格兰杰因果关系去研究它们之间的结构了,而要用偏相关因果关系进行分析。论文介绍了偏相关因果关系的基本概念,并对一个模拟的三个神经元的网络比较了格兰杰因果关系和偏相关因果关系的区别。     

基于图模型方法的Granger因果性检验

Granger因果性是衡量系统变量间动态关系的重要依据.本文利用图模型方法研究变量间的Granger因果性,建立了Granger因果图.基于条件互信息和置换检验法建立了Granger因果图结构的辨识方法,利用混沌理论中的关联积分估计相应的检验统计量,给出了统计量的渐进分布,并用所给方法研究国际主要股市间的Granger因果关系.