Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI re...

To investigate the causal relationship between China’s domestic tourism and economic growth, this paper performs co-integration analysis and Granger causality test by making use of annual time series data from 1984 to 2009. Co-integration analysis indicates that there are long-term and stable equilibrium relationships between the development of China’s domestic tourism and economic growth. The ...

Atlantic tropical cyclones have been getting stronger recently with a trend that is related to an increase in the late summer/early fall sea-surface temperature over the North Atlantic. Some studies attribute the increasing ocean warmth and hurricane intensity to a natural climate fluctuation, known as the Atlantic Multidecadal Oscillation; others suggest that climate change related to anthropo...

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 recently proposed a new measure, termed Phase Slope Index (PSI), It estimates the causal direction of interactions robustly with respect to instantaneous mixtures of independent sources with arbitrary spectral content. We compared this method to Granger Causality for linear systems containing spatially and temporarily mixed noise and found that, in contrast to PSI, the latter was not able to...

Important information on the structure of complex systems can be obtained by measuring to what extent the individual components exchange information among each other. The linear Granger approach, to detect cause-effect relationships between time series, has emerged in recent years as a leading statistical technique to accomplish this task. Here we generalize Granger causality to the nonlinear c...

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate auto...

the paper investigates the effects of interest rates on stock market performance by using monthly time series data for the economy of bangladesh over the period of 1991 to 2012. a wide range of econometric techniques have been employed to analyze the relationship between the interest rate and stock market return. the study reveals a stable and significant long run relationship between the varia...

This study examines the causal relationship between stock price and interest rate, using monthly data for the period from January 2007 to December 2013. All Share Price Index (ASPI) in Colombo Stock Exchange is used for the stock prices and the details on interest rate have been collected from the data released by the Central Bank of Sri Lanka. Augmented Dickey Fuller test was used to find out ...

This paper examines the dynamic relationship between income, energy use and carbon dioxide (CO2) emissions in Malaysia using time-series data during 1975 to 2011. This study also attempts to validate the environmental Kuznet curve (EKC) hypothesis. Applying a multivariate model of income, energy consumption in the transportation sector, carbon emissions, structural change in the economy and ren...