In this paper, we evaluate the performance of the dynamic Nielsen and Siegel interest rate model in forecasting Australian government bond yields. We compare a two‐stage OLS estimation procedure to a more powerful and robust state‐space framework estimated via a Kalman filter. We show that the one‐step approach generates smaller forecast errors than the two‐step procedure or a benchmark random walk model when forecasting the Australian government term structure across various horizons.