We investigate whether incorporating time variation and fat-tails into a suit of popular univariate and multivariate Gaussian distributed models can improve the forecast performance of key Australian macroeconomic variables: real GDP growth, CPI inflation and a short-term interest rate.

Time varying parameters and stochastic volatility with Student's-t error distribution are important modelling features of the data.

A VAR with the proposed features provides the best interest and inflation forecasts.

Remarkably, a simple rolling window AR with Student's-t errors provides the most accurate GDP forecasts.