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Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows

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  • Katrin Assenmacher-Wesche
  • M. Hashem Pesaran

Abstract

We investigate the effect of forecast uncertainty in a cointegrating vector error correction model for Switzerland. Forecast uncertainty is evaluated in three different dimensions. First, we investigate the effect on forecasting performance of averaging over forecasts from different models. Second, we look at different estimation windows. We find that averaging over estimation windows is at least as effective as averaging over different models and both complement each other. Third, we explore whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the effect of the weighting scheme on forecast accuracy is small in our application.

Suggested Citation

  • Katrin Assenmacher-Wesche & M. Hashem Pesaran, 2007. "Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows," CESifo Working Paper Series 2116, CESifo.
    • Handle: RePEc:ces:ceswps:_2116
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    1. Martin Feldkircher, 2013. "A Global Macro Model for Emerging Europe," Working Papers 185, Oesterreichische Nationalbank (Austrian Central Bank).

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    More about this item

    Keywords

    Bayesian model averaging; choice of observation window; long-run structural vector autoregression;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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