We consider a situation in which the forecaster has available M individual forecasts of a univariate target variable. We propose a 3-step procedure designed to exploit the interrelationships among the M forecast-error series (estimated from a large time-varying parameter VAR model of the errors, using past observations) with the aim of obtaining more accurate predictions of future forecast errors. The refined future forecast-error predictions are then used to obtain M new individual forecasts that are adapted to the information from the estimated VAR. The adapted M individual forecasts are ultimately combined and any potential accuracy gains of the adapted combination forecasts analyzed. We evaluate our approach in an out-of-sample forecasting analysis, using a well-established 7-country data set on output growth. Our 3-step procedure yields substantial accuracy gains (in terms of loss reductions ranging between 6.2% up to 18%) for the simple average and three time-varying-parameter combination forecasts.

中文翻译：

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一种通过 VAR 误差建模提高预测组合精度的方法

我们考虑一种情况，其中预测者对单变量目标变量有 M 个可用的单独预测。我们提出了一个 3 步程序，旨在利用 M 个预测误差序列之间的相互关系（从误差的大时变参数 VAR 模型估计，使用过去的观察），目的是获得对未来预测误差的更准确预测. 然后使用改进的未来预测误差预测来获得 M 个新的个体预测，这些预测适用于来自估计 VAR 的信息。调整后的 M 个单独预测最终被组合，并且分析调整后的组合预测的任何潜在准确度增益。我们在样本外预测分析中评估我们的方法，使用完善的 7 个国家的产出增长数据集。