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：我们采用一个模型和预测组合损失折扣框架,概括和结合了巴伊西亚模型综合和通用贝耶斯方法;我们使用损失函数来评分不同模型的性能,并采用多级折扣办法,灵活地说明模型加权的动态;这种新颖和简单的模型组合办法可以很容易地适用于大比例模型平均/选择,可以处理突变制度等不寻常的特征,并且可以适应不同的预测问题;我们将我们的方法与既定的方法和一些宏观经济预测实例的先进方法进行比较;我们发现,拟议的方法提供了一种有吸引力的、计算效率高的替代基准方法,而且往往比比较复杂的技术要好。