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

Katrin Assenmacher-Wesche () and M Hashem Pesaran ()

No 3071, IZA Discussion Papers from Institute for the Study of Labor (IZA)

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.

Keywords: Bayesian model averaging; choice of observation window; long-run structural vector autoregression (search for similar items in EconPapers)
JEL-codes: C53 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-for
Date: 2007-09

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Related works:
Working Paper: Assessing forecast uncertainties in a VECX* model for Switzerland: an exercise in forecast combination across models and observation windows (2007)
Working Paper: Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows (2007)
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