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Adaptive forecasting in the presence of recent and ongoing structural change

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  • Giraitis, Liudas
  • Kapetanios, George
  • Price, Simon

Abstract

We consider time series forecasting in the presence of ongoing structural change where both the time series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially weighted moving averages, known to be robust to historical structural change, are found also to be useful in the presence of ongoing structural change in the forecast period. A crucial issue is how to select the degree of downweighting, usually defined by an arbitrary tuning parameter. We make this choice data-dependent by minimising the forecast mean square error, and provide a detailed theoretical analysis of our proposal. Monte Carlo results illustrate the methods. We examine their performance on 97 US macro series. Forecasts using data-based tuning of the data discount rate are shown to perform well.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:econom:v:177:y:2013:i:2:p:153-170
    DOI: 10.1016/j.jeconom.2013.04.003
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Forecasting GDP in the presence of breaks: when is the past is a good guide to the future?
      by bankunderground in Bank Underground on 2015-08-20 11:30:00
    2. Forecasting GDP in the presence of breaks: when is the past a good guide to the future?
      by Guest Author in The Big Picture on 2015-09-01 14:00:11

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    Cited by:

    1. Hännikäinen Jari, 2017. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-22, January.
    2. Fabio Busetti & Pietro Cova & Antonio Maria Conti & Filippo Scoccianti & Libero Monteforte & Giordano Zevi & Valentina Aprigliano & Andrea Gerali & Alberto Locarno & Alessandro Notarpietro & Massimili, 2014. "The effects of the crisis on production potential and household spending in Italy," Workshop and Conferences 18, Bank of Italy, Economic Research and International Relations Area.
    3. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    4. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    5. Kapetanios, George & Marcellino, Massimiliano & Venditti, Fabrizio, 2016. "Large Time-Varying Parameter VARs: A Non-Parametric Approach," CEPR Discussion Papers 11560, C.E.P.R. Discussion Papers.
    6. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    7. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models," Working Papers 770, Queen Mary University of London, School of Economics and Finance.
    8. Mardi Dungey & Jan P.A.M. Jacobs & Jing Tian, 2017. "Forecasting output gaps in the G-7 countries: the role of correlated innovations and structural breaks," Applied Economics, Taylor & Francis Journals, vol. 49(45), pages 4554-4566, September.
    9. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2014. "Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," CEPR Discussion Papers 10168, C.E.P.R. Discussion Papers.
    10. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    11. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    12. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    13. Marianna Riggi & Fabrizio Venditti, 2014. "Surprise! Euro area inflation has fallen," Questioni di Economia e Finanza (Occasional Papers) 237, Bank of Italy, Economic Research and International Relations Area.
    14. Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-005, The George Washington University, Department of Economics, Research Program on Forecasting.
    15. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    16. Franses, Ph.H.B.F. & Janssens, E., 2017. "This time it is different! Or not?," Econometric Institute Research Papers EI2017-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. Andre Jungmittag, 2016. "Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 373-380, July.
    18. repec:spr:izalpo:v:6:y:2017:i:1:d:10.1186_s40173-017-0087-z is not listed on IDEAS
    19. Guido Bulligan & Eliana Viviano, 2017. "Has the wage Phillips curve changed in the euro area?," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-22, December.

    More about this item

    Keywords

    Recent and ongoing structural change; Forecast combination; Robust forecasts;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

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