To gain insight into complex systems it is a key challenge to infer nonlinear causal directional relations from observational time-series data. Specifically, estimating relationships between interacting components in large with only short recordings over few temporal observations remains an important, yet unresolved problem. Here, we introduce large-scale Nonlinear Granger Causality (lsNGC) app...

The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional roles are not clear. The state of the ar...

The present investigation examined the temporal relationships between changes in coronary artery heart disease (CAHD) mortality rates from whites (1938-1980) and changes in national measures of dietary elements, tobacco consumption, alcohol consumption, and unemployment. The magnitude and latency of the causal relationships were estimated with the use of cross-lagged correlation functions (CCFs...

In this paper we develop a dynamic discrete-time bivariate probit model, in which the conditions for Granger non-causality can be represented and tested. The conditions for simultaneous independence are also worked out. The model is extended in order to allow for covariates, representing individual as well as time heterogeneity. The proposed model can be estimated by Maximum Likelihood. Granger...

This paper develops a multivariate model to test the effectiveness of monetary and fiscal policy for the economic growth in five Asian Countries (Pakistan, India, Thailand, Indonesia and Malaysia). Most of the previous studies in this area have paid less attention to stationarity, cointegration and causality issues. The series M1 (Money Stock), Government Expenditure, GDP and exports are tested...

In recent years, the study of causality in the brain, or effective connectivity, has become increasingly important in the field of computational neuroscience. Neuroimaging modalities such as EEG, fMRI and MEG provide an immense and rich source of data. Causal analysis has the potential to help make sense of this complex data, and provide important insights about the neural basis of cognition. T...

The purpose of this paper is to empirically examine whether movements in two important measurements of the aggregates money supply, M1 and M2, help in predicting future movements in the stock market. We use single-equation multivariate autoregressive models, with the optimal lag order selected using the Akaike Information Criterion, and run two types of Granger causality tests across sequences ...

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...