Adopt co-integration and granger causality test for analyzing the influences of the deviation of three industrial structures on peasants’ income based on the model of industrial structure deviation with time sequences of peasants’ income and the deviation of three industrial structures in China Jiangxi. It’s found that there exists a long-term positive equilibrium relationship between the devia...

Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are likely to be influenced by unrecorded (lat...

Granger causality (GC) is a statistical notion of causal influence based on prediction via linear vector autoregression. For Gaussian variables it equivalent to transfer entropy, an information-theoretic measure time-directed information between jointly dependent processes. We exploit such equivalence and calculate exactly the local causality, i.e., profile transferred from driver target proces...

In this paper, we discuss the properties of mixed graphs which visualize causal relationships between the components of multivariate time series. In these Granger-causality graphs, the vertices, representing the components of the time series, are connected by arrows according to the Granger-causality relations between the variables whereas lines correspond to contemporaneous conditional associa...

the relationship between economic growth and developments in the ‎financial sector has been one of the most discussed areas in economics ‎for a long time; and the direction of causality – whether financial ‎development causes economic growth or vice versa – is by no means a ‎settled issue.‎this paper investigates the short- run and long-run granger causality ‎between financial development and e...

Granger causality analyses aim to reveal the direction of influence between brain areas by analyzing temporal precedence: if a signal change in area A consistently precedes a signal change in area B, then A Granger-causes B. fMRI-based Granger causality inferences are mediated by the hemodynamic response function which can vary across brain regions. This variability might induce a bias in Grang...

The analysis begins by using annual data for the US from 1959 to 2003 to examine the macroeconomic relationship between government expenditure on public order and safety, output and investment. In practice, total spending on public order and safety is divided up into four categories (police force, fire service, law courts and prison service) so in the second part of the analysis we test for Gra...

This study examines the Granger causality between electricity consumption and Gross Domestic Product (GDP) for Pakistan using annual data covering the period 1971 to 2007. Augmented Dickey-Fuller test and Phillips-Perron test reveal that both the series, after logarithmic transformation, are non-stationary and individually integrated at order one. Engle and Granger Cointegration test exhibits t...

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Barnett et al. in 2009 proved that Granger causality and transfer entropy causality measure are equivalent for time series which have a Gaussian distribution. Granger causality test is linear, while transfer entropy a non-linear test. Many biological and physical mechanisms show to have non-Gaussian distributions. In this paper we investigate under which conditions on probability density distri...