A loss discounting framework for model averaging and selection in time series models


We introduce a Loss Discounting Framework for forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. The framework allows large scale model averaging/selection and is also suitable for handling sudden regime changes. This novel and simple model synthesis framework is compared 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.