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Forecasting breaks and forecasting during breaks

Author

Listed:
  • 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.

Suggested Citation

  • Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2011. "Forecasting breaks and forecasting during breaks," Economics Series Working Papers 535, University of Oxford, Department of Economics.
  • Handle: RePEc:oxf:wpaper:535
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    File URL: http://www.economics.ox.ac.uk/materials/papers/4886/paper535.pdf
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    References listed on IDEAS

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    1. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    2. 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.
    3. Professor E. Philip Davis, 2001. "Some evidence on financial factors in the determination of aggregate business investment for the G7," National Institute of Economic and Social Research (NIESR) Discussion Papers 187, National Institute of Economic and Social Research.
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    Citations

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    Cited by:

    1. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    2. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    3. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0005-2 is not listed on IDEAS
    4. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    5. Giraitis, Liudas & Kapetanios, George & Price, Simon, 2013. "Adaptive forecasting in the presence of recent and ongoing structural change," Journal of Econometrics, Elsevier, vol. 177(2), pages 153-170.
    6. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    7. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    8. 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.
    9. Qin, Duo & He, Xinhua, 2012. "Modelling the impact of aggregate financial shocks external to the Chinese economy," BOFIT Discussion Papers 25/2012, Bank of Finland, Institute for Economies in Transition.
    10. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, Research Program on Forecasting.

    More about this item

    Keywords

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

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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