This paper introduces a kernel-based non-parametric inferential procedure to test for Granger causality in distribution. This test is a multivariate extension of the kernel-based Granger causality test in tail event introduced by Hong, Liu, and Wang (2009). The main advantage of this test is its ability to examine a large number of lags, with higher-order lags discounted. In addition, our test ...

This paper examines empirically the tourism impact on the long-run economic growth of Greece by using the causality analysis among real gross domestic product, real effective exchange rate and international tourism earnings. A multivariate autoregressive VAR model is applied for the examined period 1960:Ι – 2000:IV. The results of cointegration analysis suggested that there is one cointegrated ...

abstract this study uses annual data over the period 2005-2014 and the panel vecm approach to examine financial inclusion and monetary policy effectiveness in africa. the study shows that financial inclusion and monetary policy effectiveness are linked by a set of long-run relationships. policy reaction to the positive financial inclusion shock is not significant. policy reaction to the positiv...

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...

UNLABELLED We propose a likelihood ratio test (LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrap-based approach. LRT is shown to be significantly faster and statistically powerful even within non-Normal distributions. An R package named gGrang...

We propose applying the linear Granger Causality concept to very high-dimensional time series. The approach is based on integrating dimensionality reduction into a multivariate time series model. If residuals of dimensionality reduced models can be transformed back into the original space, prediction errors in the high–dimensional space may be computed, and a Granger Causality Index (GCI) is pr...

This paper is aimed at analyzing the interrelation between Foreign Direct Investment (FDI) and Energy Consumption (EC) in Mexico during period 1970-2014. To do that, we carry out a cointegration test Granger causality analysis. The empirical results from show stable link growth rates of FDI EC long run. While Granger’s that short run there unidirectional rate toward EC, while medium bidirection...

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furni...

Power modulations of oscillations in electro- and magnetoencephalographic (EEG/MEG) signals have been linked to a wide range of brain functions. To date, most of the evidence is obtained by correlating bandpower fluctuations to specific target variables such as reaction times or task ratings, while the causal links between oscillatory activity and behavior remain less clear. Here, we propose to...