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Forecasting breaks and forecasting during breaks

  • Jennifer Castle
  • David Hendry
  • Nicholas W.P. Fawcett

Success in accurately forecasting breaks requires that they are predictable from relevant information available at the forecast origin using an appropriate model form, which can be selected and estimated before the break.� To clarify the roles of these six necessary conditions, we distinguish between the information set for 'normal forces' and the ones for 'break drivers', then outline sources of potential information.� Relevant non-linear, dynamic models facing multiple breaks can have more candidate variables than observations, so we discuss automatic model selection.� As a failure to accurately forecast breaks remains likely, we augment our strategy by modelling breaks during their progress, and consider robust forecasting devices.

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File URL: http://www.economics.ox.ac.uk/materials/papers/4886/paper535.pdf
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 535.

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Date of creation: 01 Feb 2011
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Handle: RePEc:oxf:wpaper:535
Contact details of provider: Postal: Manor Rd. Building, Oxford, OX1 3UQ
Web page: http://www.economics.ox.ac.uk/
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  1. Demirguc, Asli & Detragiache, Enrica, 2000. "Monitoring Banking Sector Fragility: A Multivariate Logit Approach," World Bank Economic Review, World Bank Group, vol. 14(2), pages 287-307, May.
  2. Professor E. Philip Davis, 2001. "Some evidence on financial factors in the determination of aggregate business investment for the G7," NIESR Discussion Papers 155, National Institute of Economic and Social Research.
  3. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
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