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...

This study re-examines the long run relationship between the budget and current account deficits in an oil-dependent open economy like Nigeria using a multivariate Granger causality test within the VECM framework. This result confirmed the existence of a long run relationship between the budget and current account deficit in Nigeria, thus supporting the Mudell-Fleming theory and refuting the Ri...

It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that if this assumption is violated, then the characterization of Granger non-causality in a VAR model fail...

Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “Granger causality,” existing approaches for Granger causal inference in high-dimensional data lack the means to characterize the uncertainty associated with Granger causality estimate...

Directed partial correlation is a measure for the likelihood of a directed direct interdependence in a multivariate system. Directed partial correlation is based on vector autoregressive processes, and is able to detect Granger causality in such systems. Because inference is entirely drawn from measured data no prior knowledge on the system under investigation is needed.

This paper investigates the long run relationship between entry and exit using aggregate annual data from the Turkish manufacturing industry for the period 1968-2001. The time series properties of the data imply that simple OLS regressions may yield spurious results. We employ both bivariate and multivariate models to test for Granger causality. Utilizing relatively new time series techniques, ...

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...

The main objective of this study is to investigate the causal relationship between tourism development and economic growth in Gulf Cooperation Council (GCC) countries in a multivariate model, using panel data for the period 1995–2012. The study adopts a panel Granger causality analysis approach to assess the contribution of tourism to economic growth in GCC countries as a whole, and in each ind...

This paper examines the causality between concentration in banking industry and economic growth by using data across 15 countries named in "Iran outlook in 2025", over the period 2004-2011. Our aim is to assess whether the economy grows more or less rapidly in areas where the banking sector is more concentrated. The topic is motivated by the fact that the causality between concentration in bank...