This article employs cointegration and error-correction modelling to test the causal relationship between real income, exports and human capital stock using data for China over the period 1960 to 1999. We find that real exports, human capital and real income are cointegrated when real exports is the dependent variable, but are not cointegrated when human capital or real income are the dependent...

We propose a method of analysis of dynamical networks based on a recent measure of Granger causality between time series, based on kernel methods. The generalization of kernel-Granger causality to the multivariate case, here presented, shares the following features with the bivariate measures: (i) the nonlinearity of the regression model can be controlled by choosing the kernel function and (ii...

The rapidly growing literature on the relationship between energy consumption and economic growth has not univocally identified the ‘real’causal relationship yet. We argue that bivariate models, which analyze the causality at the level of the total economy, are not appropriate – especially in cases where both variables do not cover the same scope of economic activity. After discussing appropria...

In this research paper, attempt has been made to explore the relation especially the causal relation between stock market index i.e. BSE Sensex and three key macro economic variables of Indian economy by using correlation, unit root stationarity tests and Granger causality test. Monthly data has been used from April,1995 to March, 2009 for all the variables, like, BSE Sensex, wholesale price in...

Learning temporal causal structures among multiple time series is one of the major tasks in mining time series data. Granger causality is one of the most popular techniques in uncovering the temporal dependencies among time series; however it faces two main challenges: (i) the spurious effect of unobserved time series and (ii) the computational challenges in high dimensional settings. In this p...

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magneti...

Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C NPMR , Autoregressive modeling is replaced by Nonparametri...

Anatomical studies show the existence of corticomotor neuronal projections to the spinal cord before birth, but whether the primary motor cortex drives muscle activity in neonatal 'spontaneous' movements is unclear. To investigate this issue, we calculated corticomuscular coherence (CMC) and Granger causality in human neonates. CMC is widely used as an index of functional connectivity between t...

This paper demonstrates that linear regression models with an AR(1) error structure implicitly assume that yt does not Granger cause any of the exogenous variables in Xt. An indirect test of the common factor restrictions based on this Granger non-causality is proposed and shown to outperform existing tests. ∗Copyright 2004 by Anya McGuirk and Aris Spanos. All rights reserved.