Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This assumption might be overly restrictive could have a deleterious impact on forecasting accuracy. As solution we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting vector (BAVART) model is capable of capturing arbitrary nonlinear relations covariates wit...

Causality graphs are routinely estimated in social sciences, natural and engineering due to their capacity efficiently represent the spatiotemporal structure of multi-variate data sets a format amenable for human interpretation, forecasting, anomaly detection. A popular approach mathematically formalize causality is based on vector autoregressive (VAR) models constitutes an alternative well-kno...