Combining forecast densities from VARs with uncertain instabilities

Anne Sofie Jore, James Mitchell, Shaun Vahey

    Research output: Contribution to journalArticle


    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) vector autoregressive (VAR) and autoregressive (AR) models of output growth, inflation and interest rates. Our proposed recursive-weight density combination strategy, based on the recursive logarithmic score of the forecast densities, produces well-calibrated predictive densities for US real-time data by giving substantial weight to models that allow for structural breaks. In contrast, equal-weight combinations produce poorly calibrated forecast densities for Great Moderation data.
    Original languageEnglish
    Pages (from-to)621-634
    JournalJournal of Applied Econometrics
    Issue number4
    Publication statusPublished - 2010


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