Vector Autoregression (VAR) is a statistical modeling approach designed for the analysis of interdependencies among multiple time series variables. In VAR, two or more variables are considered simultaneously, acknowledging their dynamic relationships. The model incorporates a lag order (p) indicating the number of past observations considered for each variable and assumes stationarity or enforces it through differencing. Coefficient matrices capture the impact of past values of all variables on the present values of each variable. Key steps in VAR modeling include data exploration, stationarity testing, lag order selection, parameter estimation, and diagnostic checking. VAR models are widely employed in economics, finance, and other domains to uncover complex interactions between variables, facilitate forecasting, and assess policy implications through tools like impulse response functions and Granger causality tests.