This paper uses panel Granger causality tests to study the relationship between sector specific FDI and CO2 emissions. Using a sample of 18 Latin American countries for the 1980-2007 period, we find causality running from FDI in polluting intensive industries (“the dirty sector”) to CO2 emissions per capita. This result is robust to controlling for other factors associated with CO2 emissions an...

Barnett et al. in 2009 proved that Granger causality and transfer entropy causality measure are equivalent for time series which have a Gaussian distribution. Granger causality test is linear, while transfer entropy a non-linear test. Many biological and physical mechanisms show to have non-Gaussian distributions. In this paper we investigate under which conditions on probability density distri...

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framewo...

Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes — Hawkes process. According to the relationship between Hawkes process’s impact function and its Granger causality graph, our model represents impact functions using a series of basis f...

This study analyzes the causal relationship between oil prices and production costs their implication over hedging in companies. Two different data sets have been used as a proxy for costs: i) monthly product price index related to activities from Bureau of Labour Statistics; ii) yearly obtained reports publicly traded For price, future contracts Brent (1M, 12M, 24M, 60M) explored. Based on Gra...

Granger causality is a statistical concept of causality that is based on prediction. According to Granger causality, if a signal X1 "Granger-causes" (or "G-causes") a signal X2, then past values of X1 should contain information that helps predict X2 above and beyond the information contained in past values of X2 alone. Its mathematical formulation is based on linear regression modeling of stoch...

Granger causality (GC) is a statistical notion of causal influence based on prediction via linear vector autoregression. For Gaussian variables it equivalent to transfer entropy, an information-theoretic measure time-directed information between jointly dependent processes. We exploit such equivalence and calculate exactly the local causality, i.e., profile transferred from driver target proces...

a r t i c l e i n f o JEL classification: C23 E44 Q43 Keywords: Crude oil shocks Stock market prices Panel data Asymmetric adjustment Granger causality This paper proposes a panel threshold cointegration approach to investigate the relationship between crude oil shocks and stock markets for the OECD and non-OECD panel from January 1995 to December 2009. Nonlinear cointegration is confirmed for ...

The main concern of this paper is to answer the question of the determinants of FDI inflows to West African Economic and Monetary Union (WAEMU). The literature on FDI recognizes not only the existence of gaps between domestic savings and investment in most developing countries but also that FDI constitutes a cure capable to bring the latest technology and management know-how into these countrie...

Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between s...