• Structural change;
  • Forecast combination;
  • Varying-parameter models


Forecasters are generally concerned about the properties of model-based predictions in the presence of structural change. In this paper, it is argued that forecast errors can under those conditions be greatly reduced through systematic combination of forecasts. We propose various extensions of the standard regression-based theory of forecast combination. Rolling weighted least squares and time-varying parameter techniques are shown to be useful generalizations of the basic framework. Numerical examples, based on various types of structural change in the constituent forecasts, indicate that the potential reduction in forecast error variance through these methods is very significant. The adaptive nature of these updating procedures greatly enhances the effect of risk-spreading embodied in standard combination techniques.