T his empirical analysis endeavors to trace out the causal nexus between core inflation and economic growth from the perspective of twenty worlds’ leading economy with the help of the nonlinear Granger causality approach by using time series data from 1981 to 2016. Based on nonlinear Granger causality results, it has been found that there is unidirectional casualty running from core ...

iran is one of the most concentrated areas of afghan migrants and refugees. most afghans dispersed throughout the country mixed with iranian households. the majority of afghan immigrants are typically unskilled and illegal workers. this study aims at investigating the nature of the causal relationship between afghan immigrants and two main labor market indicators, average wages and unemployment...

Recently we have presented dynamic condition response structures (DCR) as a declarative process model conservatively generalizing labelled event structures to allow for finite specifications of repeated, possibly infinite behavior and acceptance conditions for infinite runs. The key ideas are to split the causality relation of event structures in two dual relations: the condition relation and t...

The study is aimed to test causal relationship between growth and unemployment, using time series data for Pakistan from 1972 to 2006. Growth is considered to be a pathway to decrease the level of unemployment. Unemployment is a social and political issue. It is a phenomenon where human resources are wasted leading to deacceleration in growth. Johanson Cointegration shows that there is long run...

This paper investigates the relationship between electricity consumption and economic growth by using Autoregressive Distributed Lag (ARDL) bounds testing approach and vector error-correction models (VECM) in Cameroon, Cote D'Ivoire, Congo, Ethiopia, Gabon, Ghana, Guatemala, Kenya, Senegal, Togo and Zambia for period 1970-2010. The ARDL results show that there is cointegration relation between ...

Databases have collected masses of information concerning cell signaling pathways that includes information on pathways, molecular interactions as well as molecular complexes. However we have no general data model to represent comprehensive properties of cell signaling pathways, so that this type of information has been represented by two different data models that we call 'binary relation' and...

Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discovery consists in fitting VAR models with an additional sparsity-promoting regu...

This paper presents experimental studies that investigate two issues related to the expression of causality in French: (i) what is the impact of order (cause-consequence vs. consequence-cause) in the processing of causally linked sentences without connectives and (ii) how, if at all, the aspectual distinction influences the nature of causal relations and their processing. Our hypothesis is that...

The task of detecting causal connections in text would benefit greatly from a comprehensive representation of Cause and Effect in WordNet, since previous studies show that semantic abstractions play an important role in the linguistic detection of semantic relations, in particular the cause-effect relation. Based on these studies on causality, and on our own general intuitions about causality, ...