A loss discounting framework for model averaging and selection in time series models
提出一个损失贴现框架用于模型和预测组合，能灵活处理模型权重的动态变化，适用于大规模模型平均与选择，在宏观经济预测中表现优于复杂方法。
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 that allows for 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, handle unusual features such as sudden regime changes and be tailored to different forecasting problems. We compare our method to established and state-of-the-art methods for several macroeconomic forecasting examples. The proposed method offers an attractive, computationally efficient alternative to the benchmark methodologies and often outperforms more complex techniques.