用于时间序列模型中模型平均与选择的损失折扣框架
A loss discounting framework for model averaging and selection in time series models
AI总结：
提出一种结合贝叶斯模型合成与广义贝叶斯方法的损失折扣框架，通过多级折扣机制灵活设定模型权重，可应用于大规模模型平均与选择，在宏观经济预测中计算高效且优于复杂技术。
摘要：
我们提出了一种用于模型与预测组合的损失折扣框架，该框架推广并结合了贝叶斯模型合成与广义贝叶斯方法。我们使用损失函数评估不同模型的性能，并引入了一种多级折扣机制，允许灵活设定模型权重的动态变化。这种新颖且简单的模型组合方法可轻松应用于大规模模型平均与选择，能够处理如突发机制变化等异常特征，并可针对不同的预测问题进行定制。我们在多个宏观经济预测示例中，将该方法与既有方法及最先进方法进行了比较。结果表明，所提方法提供了一种有吸引力且计算高效的基准方法替代方案，且通常优于更复杂的技术。
英文摘要：
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.
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