This paper investigates the relationship between exports and economic growth in the three of the largest exporting countries in the world, such as European Union, United States of America and Japan. For this purpose we have used Granger causality analysis based on error correction model. The results of this paper suggested that exports have a causal effect on the development process for the cou...

The current paper investigates the existence and nature of the Granger causality between electricity consumption and economic growth for 17 industries in Taiwan. Empirical results over the period 1998–2014 suggest that a panel cointegration test shows a long-run equilibrium relationship and a bi-directional Granger causality between electricity and economic growth has been found. The result ind...

The concept of causality is naturally defined in terms of conditional distribution, however almost all the empirical works focus on causality in mean. This paper aim to propose a nonparametric statistic to test the conditional independence and Granger non-causality between two variables conditionally on another one. The test statistic is based on the comparison of conditional distribution funct...

It is generally believed that the noise variance in in vivo neuronal data exhibits time-varying volatility, particularly signal-dependent noise. Despite a widely used and powerful tool to detect causal influences in various data sources, Granger causality has not been well tailored for time-varying volatility models. In this technical note, a unified treatment of the causal influences in both m...

The functional connectivity between cortical neurons is not static and is known to exhibit contextual modulations in terms of the coupling strength. Here we hypothesized that the information flow in a cortical local circuit exhibits complex forward-and-back dynamics, and conducted Granger causality analysis between the neuronal spike trains that were simultaneously recorded from macaque inferio...

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained tract...

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causa...

The purpose of this paper is to analyze in bivariate vector autoregression the relationship between feedback in stochastic systems, Granger causality and a measure of dissimilarity between ARMA models. In particular, we consider a bivariate vector autoregressive processes of order p (a bivariate VAR(p) process) and we prove if the distance between the univariate ARMA models implied by the VAR r...

BACKGROUND Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks m...