Combining Forecast Densities from VARs with Uncertain Instabilities
Jore, A. S., Vahey, S. P.
Anne Sofie Jore
Clark and McCracken (2008) argue that combining real-time point forecasts from VARs of output, prices and interest rates improves point forecast accuracy in the presence of uncertain model instabilities. In this paper, we generalize their approach to consider forecast density combinations and evaluations. Whereas Clark and Mc-Cracken (2008) show that the point forecast errors from particular equal-weight pair wise averages are typically comparable or better than benchmark univariate time series models, we show that neither approach produces accurate real-time forecast densities for recent US data. If greater weight is given to models that allow for the shifts in volatilities associated with the Great Moderation, predictive density accuracy improves substantially.
Recursive-weight forecast combination is often found to an ineffective method of improving point forecast accuracy in the presence of uncertain instabilities. We examine the effectiveness of this strategy for forecast densities using (many) VARs and ARs of output, prices and interest rates. Our proposed recursive-weights density combination strategy, based on the recursive logarithmic score of the forecast densities, produces accurate predictive densities by giving substantial weight to models that allow for structural breaks. In contrast, equal-weight combinations produce poor real-time US forecast densities for Great Moderation data. Classification-C32, C53, E37
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