Multi-step forecasting in the presence of breaks

This paper analyzes the relative performance of multi-step AR forecasting methods in the presence of breaks and data revisions. Our Monte Carlo simulations indicate that the type and timing of the break affect the relative accuracy of the methods. The iterated autoregressive method typically produces more accurate point and density forecasts than the alternative multi-step AR methods in unstable environments, especially if the parameters are subject to small breaks. This result holds regardless of whether data revisions add news or reduce noise. Empirical analysis of real-time US output and inflation series shows that the alternative multi-step methods only episodically improve upon the iterated method.