We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting and...

We examine how to forecast after a recent break, introducing a new approach, monitoring for change and then combining forecasts from a model using the full sample and another using post‐break data. We compare this to some robust techniques: rolling regressions, forecast averaging over all...