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Forecasting with Breaks

Listed author(s):
  • Clements, Michael P.
  • Hendry, David F.

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.

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This chapter was published in:
  • G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
  • This item is provided by Elsevier in its series Handbook of Economic Forecasting with number 1-12.
    Handle: RePEc:eee:ecofch:1-12
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