Forecasting Using Bayesian and Information-Theoretic Model Averaging

Abstract

Model averaging often improves forecast accuracy over individual forecasts. It may also be seen as a means of forecasting in data-rich environments. Bayesian model averaging methods have been widely advocated, but a neglected frequentist approach is to use information-theoretic-based weights. We consider the use of information-theoretic model averaging in forecasting U.K. inflation, with a large dataset, and find that it can be a powerful alternative to Bayesian averaging schemes.