Description:
A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonomies of all sources of forecast errors for both conditional mean and conditional variance processes, we consider the impacts of breaks and their relevance in forecasting models: (a) where the breaks occur after forecasts are announced; and (b) where they occur in-sample and hence pre-forecasting. The impact on forecasts depends on which features of the models are non-constant. Different models and methods are shown to fare differently in the face of breaks. While structural breaks induce an instability in some parameters of a particular model, the consequences for forecasting are specific to the type of break and form of model. We present a detailed analysis for cointegrated VARs, given the popularity of such models in econometrics. We also consider the detection of breaks, and how to handle breaks in a forecasting context, including ad hoc forecasting devices and the choice of the estimation period. Finally, we contrast the impact of structural break non-constancies with non-constancies due to non-linearity. The main focus is on macro-economic, rather than finance, data, and on forecast biases, rather than higher moments. Nevertheless, we show the relevance of some of the key results for variance processes. An empirical exercise ‘forecasts’ UK unemployment after three major historical crises.