This paper compares the forecasting performance of different models
which have been proposed for forecasting in the presence of structural
breaks. These models differ in their treatment of the break process, the
model which applies in each regime and the out- of-sample probability
of a break occurring. In an extensive empirical evaluation involving 60
macroeconomic quarterly and monthly time series, we demonstrate the
presence of structural breaks and their importance for forecasting in the
vast majority of cases. We find no single forecasting model that
consistently works best in the presence of structural breaks. In many
cases, the formal modeling of the break process is important in
achieving good forecast per- formance. However, there are also many
cases where simple, rolling window based forecasts perform well.