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Forecasting Under Structural Change

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Empirical Economic and Financial Research

Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 48))

Abstract

Forecasting strategies that are robust to structural breaks have earned renewed attention in the literature. They are built on weighted averages downweighting past information and include forecasting with rolling window, exponential smoothing or exponentially weighted moving average and forecast pooling. These simple strategies are particularly attractive because they are easy to implement, possibly robust to different types of structural change and can adjust for breaks in real time. This review introduces the dynamic model to be forecast, explains in detail how the data-dependent tuning parameter for discounting the past data is selected and how basic forecasts are constructed and the forecast error estimated. It comments on the forecast error and the impact of weak and strong dependence of noise on the quality of the prediction. It also describes various forecasting methods and evaluates their practical performance in robust forecasting.

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References

  • Ang, A., & Bekaert, G. (2002). Regime switches in interest rates. Journal of Business & Economic Statistics, 20(2), 163–182.

    Article  Google Scholar 

  • Clark, T. E., & McCracken, M. W. (2010). Averaging forecasts from VARs with uncertain instabilities. Journal of Applied Econometrics, 25(1), 5–29.

    Article  Google Scholar 

  • Clements, M. P., & Krolzig, H. M. (1998). A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP. The Econometrics Journal, 1(1), 47–75.

    Article  Google Scholar 

  • D’Agostino, A., Gambetti, L., & Giannone, D. (2013). Macroeconomic forecasting and structural change. Journal of Applied Econometrics, 28, 82–101.

    Article  Google Scholar 

  • Diebold, F. X., & Inoue, A. (2001). Long memory and regime switching. Journal of Econometrics, 105(1), 131–159.

    Article  Google Scholar 

  • Eklund, J., Kapetanios, G., & Price, S. (2010). Forecasting in the Presence of Recent Structural Change. Bank of England Working Paper, 406.

    Google Scholar 

  • Garcia, R., & Perron, P. (1996). An analysis of the real interest rate under regime shifts. The Review of Economics and Statistics, 79, 111–125.

    Article  Google Scholar 

  • Giraitis, L., Kapetanios, G., & Price, S. (2013). Adaptive forecasting in the presence of recent and ongoing structural change. Journal of Econometrics, 177, 153–170.

    Article  Google Scholar 

  • Gourieroux, C., & Jasiak, J. (2001). Memory and infrequent breaks. Economics Letters, 70(1), 29–41.

    Article  Google Scholar 

  • Granger, C. W., & Hyung, N. (2004). Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. Journal of Empirical Finance, 11(3), 399–421.

    Article  Google Scholar 

  • Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384.

    Article  Google Scholar 

  • Hendry, D. F. (2000). On detectable and non-detectable structural change. Structural Change and Economic Dynamics, 11(1), 45–65.

    Article  Google Scholar 

  • Kapetanios, G. (2006). Nonlinear autoregressive models and long memory. Economics Letters, 91(3), 360–368.

    Article  Google Scholar 

  • Koop, G., & Potter, S. M. (2007). Estimation and forecasting in models with multiple breaks. The Review of Economic Studies, 74(3), 763–789.

    Article  Google Scholar 

  • Maheu, J. M., & Gordon, S. (2008). Learning, forecasting and structural breaks. Journal of Applied Econometrics, 23(5), 553–583.

    Article  Google Scholar 

  • Mansur, M. (2013). Ph.D. thesis. Queen Mary, University of London.

    Google Scholar 

  • Pesaran, M. H., Pettenuzzo, D., & Timmermann, A. (2006). Forecasting time series subject to multiple structural breaks. The Review of Economic Studies, 73(4), 1057–1084.

    Article  Google Scholar 

  • Pesaran, M. H., & Pick, A. (2011). Forecast combination across estimation windows. Journal of Business & Economic Statistics, 29(2), 307–318.

    Article  Google Scholar 

  • Pesaran, M. H., & Timmermann, A. (2002). Market timing and return prediction under model instability. Journal of Empirical Finance, 9(5), 495–510.

    Article  Google Scholar 

  • Pesaran, M. H., & Timmermann, A. (2007). Selection of estimation window in the presence of breaks. Journal of Econometrics, 137(1), 134–161.

    Article  Google Scholar 

  • Rossi, B. (2012). Advances in forecasting under instability. In G. Elliott & A. Timmermann (Eds.), Handbook of economic forecasting. North Holland: Elsevier.

    Google Scholar 

  • Stock, J. H., & Watson, M. W. (1996). Evidence on structural instability in macroeconomic time series relations. Journal of Business & Economic Statistics, 14(1), 11–30.

    Google Scholar 

  • Stock, J. H., & Watson, M. W. (2007). Why has US inflation become harder to forecast? Journal of Money, Credit and Banking, 39(s1), 3–33.

    Article  Google Scholar 

  • Timmermann, A. (2001). Structural breaks, incomplete information, and stock prices. Journal of Business & Economic Statistics, 19(3), 299–314.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Queen Mary, University of London, London, UK

    Liudas Giraitis, George Kapetanios & Mohaimen Mansur

  2. Bank of England, London, UK

    Simon Price

  3. City University, London, UK

    Simon Price

Authors
  1. Liudas Giraitis
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  2. George Kapetanios
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  3. Mohaimen Mansur
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  4. Simon Price
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Corresponding author

Correspondence to Liudas Giraitis .

Editor information

Editors and Affiliations

  1. Dept. of Mathematics and Statistics, University of Konstanz, Konstanz, Germany

    Jan Beran

  2. Faculty of Business Administration and Economics, University of Paderborn, Paderborn, Germany

    Yuanhua Feng

  3. Helmut Schmidt University, Hamburg, Germany

    Hartmut Hebbel

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© 2015 Springer International Publishing Switzerland

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Giraitis, L., Kapetanios, G., Mansur, M., Price, S. (2015). Forecasting Under Structural Change. In: Beran, J., Feng, Y., Hebbel, H. (eds) Empirical Economic and Financial Research. Advanced Studies in Theoretical and Applied Econometrics, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-03122-4_25

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