Bayesian Autoregression

We present the basics of Bayesian estimation and inference for autoregressive models. The range of topics includes the natural conjugate analysis using normal-inverted-gamma 2 prior distribution and its extensions focusing on hierarchical modelling, conditional heteroskedasticity, and Student-t error terms. We focus on forecasting and sampling from the predictive density.