A question of great interest in systems biology is how to uncover complex network structures from experimental data[1, 3, 18, 38, 55]. With the rapid progress of experimental techniques, a crucial task is to develop methodologies that are both statistically sound and computationally feasible for analysing increasingly large datasets and reliably inferring biological interactions from them [16, ...

Quantitative characterization of interaction between processes from time series is often required in different fields of natural science including geophysics and biophysics. Typically, one estimates "short-term" influences, e.g., the widely used Granger causality is defined via one-step-ahead predictions. Such an approach does not reveal how strongly the "long-term" behavior of one process unde...

We develop a horizontal R&D growth model that allows us to investigate the different channels through which financial reforms affect investment and patent activity. First, “micro” reform abolishes barriers entry in banking sector produces straightforward result: decrease lending rates stimulates economic growth. Second, “macro” removes restrictions on banks’ reserves credit controls. While this...

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using structural equation modeling (SEM) and, more recently, with G...

Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. It has long been recognized that neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understandin...

This study reexamines the causal link between electricity consumption, economic growth and CO2 emissions in the BRICS countries (i.e., Brazil, Russia, India, China, and South Africa) for the period 1990– 2010, using panel causality analysis, accounting for dependency and heterogeneity across countries. Regarding the electricity–GDP nexus, the empirical results support evidence on the feedback h...

The study examines the asymmetric effect of oil price on exchange rate and stock using Nonlinear Autoregressive Distributive Lag (NARDL) technique time-series data spanning from January 1996 to September 2020. multivariate cointegration test showed evidence a long-run relationship among price, rate, price. linear Granger causality that is granger caused by cause rate. nonlinear nonlinearity BDS...