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Forecasting breaks and forecasting during breaks

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

Abstract

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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Bibliographic Info

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

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Related research

Keywords: Economic forecasting; Structural breaks; Information sets; Non-linearity;

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  1. Enrica Detragiache & Asli Demirgüç-Kunt, 1999. "Monitoring Banking Sector Fragility," IMF Working Papers 99/147, International Monetary Fund.
  2. Eitrheim, Øyvind & Teräsvirta, Timo, 1995. "Testing the Adequacy of Smooth Transition Autoregressive Models," Working Paper Series in Economics and Finance 56, Stockholm School of Economics.
  3. 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.
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Cited by:
  1. Liudas Giraitis & George Kapetanios & Simon Price, 2012. "Adaptive Forcasting in the Presence of Recent and Ongoing Structural Change," CAMA Working Papers 2012-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  2. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.

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