Granger causality (GC) and its extension have been used widely to infer causal relationships from multivariate time series generated from biological systems. GC is ideally suited for causal inference in bivariate vector autoregressive process (VAR). A zero magnitude of the upper or lower off-diagonal element(s) in a bivariate VAR is indicative of lack of causal relationship in that direction re...

The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is typically done by a least squares procedure. However, this estimation method is unreliable when outliers are present in the data, and therefore we propose to estimate the vector autoregressive model by using a multivariate least trimmed squares estimator. We also show how the order...

BACKGROUND In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied. METHODS We propose to analyse the temporal interrelationships amon...

This paper reports the short-term forecasting power of the large Global Vector Autoregressive (GVAR) model originally developed by Pesaran, Schuermann, and Weiner (2004a, 2004b; PSW), and subsequently fine-tuned and re-estimated over the period 1979Q1–2003Q4 by Dees, di Mauro, Pesaran, and Smith (2007; DdPS).1 The GVAR model explicitly specifies interdependencies between different countries and...

This paper investigates the extent of volatility or risk spillovers between currency carry trade and asset markets, namely equity bond in South Africa to infer connectivity two markets. The operation examined this involves strategies, both which use African rand as investment currency, with U.S. dollar Japanese yen funding currencies. vector autoregressive BEKK-Generalised Autoregressive Condit...