Vector Autoregressive

Vector Autoregressive (VAR) models analyze the interdependencies between multiple time series, aiming to understand and predict their evolution. Current research focuses on enhancing VAR's capabilities by incorporating non-linear relationships through techniques like kernel methods and neural networks, as well as improving the interpretability of results, often by incorporating Bayesian methods and sparsity-inducing penalties to identify causal relationships. These advancements are improving the accuracy and efficiency of VAR models across diverse applications, from climate modeling to neuroscience, where understanding complex interactions between multiple variables is crucial.