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Optimal forecasts in the presence of structural breaks

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  • Pesaran, M. Hashem
  • Pick, Andreas
  • Pranovich, Mikhail

Abstract

This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for one step ahead forecasts. Under continuous breaks, our approach largely recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for optimal weights in models with a single regressor, and asymptotically valid weights for models with more than one regressor. It is shown that in these cases the optimal weight is the same across observations within a given regime and differs only across regimes. In practice, where information on structural breaks is uncertain, a forecasting procedure based on robust optimal weights is proposed. The relative performance of our proposed approach is investigated using Monte Carlo experiments and an empirical application to forecasting real GDP using the yield curve across nine industrial economies.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 177 (2013)
Issue (Month): 2 ()
Pages: 134-152

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Handle: RePEc:eee:econom:v:177:y:2013:i:2:p:134-152

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Forecasting; Structural breaks; Optimal weights; Robust optimal weights; Exponential smoothing;

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References

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Citations

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Cited by:
  1. Boonsoo Koo & Myung Hwan Seo, 2013. "Structural-break models under mis-specification: implications for forecasting," Monash Econometrics and Business Statistics Working Papers 11/13, Monash University, Department of Econometrics and Business Statistics.
  2. Taylor, Nick, 2014. "The rise and fall of technical trading rule success," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 286-302.
  3. Gunnar Bårdsen & Dag Kolsrud & Ragnar Nymoen, 2012. "Forecast robustness in macroeconometric models," Working Paper Series 13712, Department of Economics, Norwegian University of Science and Technology.
  4. Agnieszka Markiewicz & Andreas Pick, 2013. "Adaptive Learning and Survey Data," CDMA Working Paper Series 201305, Centre for Dynamic Macroeconomic Analysis.
  5. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.

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