Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting

In the new CAMP working paper 01/2019, McAlinn, Aastveit, Nakajima and West extends the foundational Bayesian predictive synthesis (BPS) framework to the multivariate setting. They present new methodology and a case study in use of a class of BPS models for multivariate forecasting.

Their case study highlights the potential of BPS to improve forecasts of multiple series at multiple forecast horizons. The approach has the potential to enable decision makers to dynamically calibrate, learn, and update predictions based on ranges of forecasts from sets of models, as well as from more subjective sources such as individual forecasters or agencies.

012019

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