Combining forecast densities from VARs with uncertain instabilities
研究了在存在不确定不稳定性时，基于递归对数得分的密度组合策略，能有效提升美国实时产出增长、通胀和利率的预测密度校准性，对宏观预测研究者有参考价值。
Abstract 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. Copyright © 2010 John Wiley & Sons, Ltd.