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This time it is different! Or not?

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  • Franses, Ph.H.B.F.
  • Janssens, E.

Abstract

We employ a simple method based on logistic weighted least squares to diagnose which past data are less or more useful for predicting the future course of a variable. A simulation experiment shows its merits. An illustration for monthly industrial production series for 17 countries suggests that earlier data are useful, for the prediction in a crisis period (2006-2011) and for the period after the crisis (2011-2016). Hence, this time, apparently it was not that different after all.

Suggested Citation

  • 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.
  • Handle: RePEc:ems:eureir:101764
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    References listed on IDEAS

    as
    1. 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.
    2. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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    Keywords

    Forecasting; Weighted Least Squares; Discounting; Logistic function; Industrial Production;

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