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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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Author Info
Katrin Assenmacher-Wesche () (Swiss National Bank)
M. Hashem Pesaran () (Cambridge University, CIMF USC and IZA)

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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.

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Publisher Info
Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3071.

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Length: 55 pages
Date of creation: Sep 2007
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Handle: RePEc:iza:izadps:dp3071

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Related research
Keywords: Bayesian model averaging; choice of observation window; long-run structural vector autoregression;

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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