There is a considerable volume of literature concerned with point forecasting which aims to assess whether producing aggregate forecasts as the sum of the components’ forecasts is better than alternative direct methods, whereas aggregate density forecasting from disaggregate components is still a relatively unexplored field. This paper develops an implementation of the bottom-up approach that is capable of producing well-performing and competitive density forecasts. This is achieved by accounting explicitly for the interaction between components, using Bayesian VARs to estimate the whole multivariate process and produce the aggregate forecasts. An empirical application using CPI and GDP data shows that the method can be used to produce aggregate density forecasts capable of accounting for the events resulting from the crisis. This suggests that it might be particularly useful for forecasting in turbulent times and therefore prove a valuable addition to the forecaster’s toolkit.

中文翻译：

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使用贝叶斯VAR从分解成分中预测聚合密度

有大量有关点预测的文献，其目的在于评估将总预测作为组成部分的预测总和是否比替代直接方法更好，而从分解组成部分进行总密度预测仍然是一个相对未开发的领域。本文开发了一种自底向上方法的实现，该方法能够生成性能良好且具有竞争力的密度预测。这是通过使用贝叶斯VAR明确估计组件之间的相互作用来实现的，以估计整个多元过程并生成汇总预测。使用CPI和GDP数据的经验应用表明，该方法可用于生成能够解释危机造成的事件的聚集密度预测。