Vector autoregressions (VAR) are statistical models used to capture the relationship between multiple time series variables. They analyze how each variable in a system influences itself and the others over time, allowing for the examination of dynamic interactions. This approach is particularly useful in economics and finance, where variables like GDP, inflation, and interest rates are interrelated. In a VAR model, each variable is regressed on its own past values and the past values of all other variables in the system. This enables researchers to forecast future values and understand the impact of shocks or changes in one variable on the others. VAR models are widely used for policy analysis and economic forecasting.