The Vector AutoRegressive (VAR) Model is a popular model for the analysis of a multivariate time series. It allows to investigate the impact changes in one time series have on other ones. A drawback of the VAR is the risk of overparametrization because the number of parameters increases quadratically with the number of included time series. This undermines the ability to identify important relationships in the data and to make accurate forecasts. In high dimensions, we therefore use sparse estimation.