This paper uses panel cointegration techniques to examine the causal relationship between renewable energy consumption, economic growth and CO2 emissions for a group of 12 MENA countries covering the annual period 1975-2008. The Granger-causality results indicate that there is no causal relationship between these variables in short run except a unidirectional causality running from renewable en...

Causality testing, the act of determining cause and effect from measurements, is widely used in physics, climatology, neuroscience, econometrics and other disciplines. As a result, a large number of causality testing methods based on various principles have been developed. Causal relationships in complex systems are typically accompanied by entropic exchanges which are encoded in patterns of dy...

We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specif...

We propose new tests for panel cointegration by extending the panel unit root tests of Choi [2001] and Maddala and Wu [1999] to the panel cointegration case. The tests are flexible, intuitively appealing and relatively easy to compute. We investigate the finite sample behavior in a simulation study. Several variants of the tests compare favorably in terms of both size and power with other widel...