We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for...

This paper examines the linear and nonlinear relationship between daily confirmed COVID-19 cases sectoral stock market volatility in India. The Granger causality test reveals bidirectional causality. Further, we observe that exists . implies historical lagged information can have a significant role predicting market.

Granger causality analysis (GCA) has been well-established in the brain imaging field. However, the structural underpinnings and functional dynamics of Granger causality remain unclear. In this paper, we present fiber-centered GCA studies on resting state fMRI and natural stimulus fMRI datasets in order to elucidate the structural substrates and functional dynamics of GCA. Specifically, we extr...

T he Causal relationship between financial development and economic growth has received divergent views in the literature under the traditional Granger approach to causality using data from various countries. The more recent Toda and Yamamoto and Dolado and Lütkepohl (TYDL) approach to causality were used to investigate the causal relationship between financial development and econom...

This study employs the bounds test for co-integration and Granger causality tests to investigate the long-run equilibrium relationship between financial development, international trade and real income growth. Furthermore we are intersting in finding the direction of causality among these economic variablesfor the Cyprus economy. The results of the study reveal that financial development as mea...

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...

The asymptotic behavior of the Granger-causality test under stochastic nonstationarity is studied. Our results confirm that the inference drawn from the test is not reliable when the series are integrated to the first order. In the presence of deterministic components, the test statistic diverges, eventually rejecting the null hypothesis, even when the series are independent of each other. More...