Pardeep Kaurah, MSc; Andrée MacMillan, MSc; Niki Boyd, MSc, PhD; Janine Senz, BSc; Alessandro De Luca, BSc; Nicki Chun, MS; Gianpaolo Suriano, PhD; Sonya Zaor, MSc; Lori Van Manen, MS; Cathy Gilpin, MS; Sarah Nikkel, MD; Mary Connolly-Wilson, Med; Scott Weissman, MS; Wendy S. Rubinstein, MD; Courtney Sebold, MS; Robert Greenstein, MD; Jennifer Stroop, MS; Dwight Yim, MD; Benoit Panzini, MD; Wen...

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...

This paper considers an extension of the univariate autoregressive conditional duration model to which durations from a second stock are added. The model is empirically used to study durations in two traded stocks, Ericsson B and AstraZeneca, on the Stockholm Stock Exchange. It is found that including durations from a second stock may add explanatory power to the univariate model. Ericsson B is...

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...