论文标题
时间序列模型中的模型平均和选择的损失折现框架
A loss discounting framework for model averaging and selection in time series models
论文作者
论文摘要
我们引入了模型和预测组合的损失折现框架，该框架概括并结合了贝叶斯模型合成和广义贝叶斯方法。我们使用损失函数来评分不同模型的性能，并引入多级折现方案，该方案允许对模型权重的动力学进行灵活的规范。这种新颖而简单的模型组合方法可以轻松地应用于大型模型平均/选择，可以处理异常功能，例如突然的制度变化，并且可以针对不同的预测问题量身定制。我们将我们的方法与建立的方法和最新方法的方法进行了比较，以进行许多宏观经济预测的例子。我们发现所提出的方法提供了一种有吸引力的计算有效替代基准方法，并且通常优于更复杂的技术。
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.
