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

Katrin Assenmacher and Mohammad Pesaran

No 3071, IZA Discussion Papers from Institute of Labor Economics (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: choice of observation window; long-run structural vector autoregression; Bayesian model averaging (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2007-09
New Economics Papers: this item is included in nep-ets and nep-for
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Published - published in: National Institute Economic Review, 2008, 203 (1), 91–108

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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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