Evaluating vector autoregressive (VAR) models
After fitting a VAR model, the next step is to evaluate how well the model captures the interactions and dynamic relationships between the different endogenous variables (multiple time series). Understanding these relationships can help you asses causality, how one variable influences another, and how shocks to one variable propagate through the system.
In this recipe, you will continue where you left off from the previous recipe, Forecasting multivariate time series data using VAR, and explore various diagnostic tools to deepen your understanding of the VAR model. Specifically, test for granger causality, analyze Residual Autocorrelation Function (ACF) plots, use the Impulse Response Function (IRF), and perform Forecast Error Variance Decomposition (FEVD).
These evaluation steps will help you understand both the causal relationships and the interdependencies between the variables in your system, ensuring that your model captures the underlying...
