1 · From AR(1) to VAR (joint dynamics)
How stacking several AR equations into one system lets each variable depend on the lagged values of all the others.
Vector Autoregression (VAR)
Joint dynamics of several series: estimation, impulse responses, Granger causality.
Course overview
Learn Vector Autoregression (VAR) interactively
This free course uses real Python and statsmodels in your browser. Work through the modules below with explanations, executable examples, interactive controls, and questions.
2 · Estimation & lag selection
How a VAR is fit equation-by-equation with OLS, and how information criteria pick the lag length p.
3 · Impulse-response functions & FEVD
Tracing how a one-time shock to one variable ripples through the system over time, and how to split forecast uncertainty across shocks.
4 · Granger causality & forecasting
Testing whether one variable's past helps predict another, and producing and reading multi-step VAR forecasts.