We introduce a Loss Discounting Framework for model and forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models and introduce a multilevel discounting scheme which allows a flexible specification of the dynamics of the model weights. This novel and simple model combination approach can be easily applied to large scale model averaging/selection, can handle unusual features such as sudden regime changes, and can be tailored to different forecasting problems. We compare our method to both established methodologies and state of the art methods for a number of macroeconomic forecasting examples. We find that the proposed method offers an attractive, computationally efficient alternative to the benchmark methodologies and often outperforms more complex techniques.

翻译：我们提出了一种用于模型与预测组合的损失折现框架，该框架概括并融合了贝叶斯模型综合与广义贝叶斯方法。我们利用损失函数对不同模型的表现进行评分，并引入一种多层折现机制，从而能够灵活指定模型权重的动态变化。这种新颖且简单的模型组合方法易于应用于大规模模型平均/选择，可处理突变体制转换等异常特征，并能针对不同的预测问题进行定制。我们将该方法与既有方法论及多项宏观经济预测案例中的前沿技术进行了比较。结果表明，所提出的方法为基准方法论提供了一种具有吸引力且计算高效的替代方案，且通常优于更复杂的技术手段。