This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible...

Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible parametric model that accommodates virtually any of these specifications—and does so in a simple way that allows for straightforward Bayesian...

Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as...

The macroeconomy is a complicated dynamic system with significant uncertainties that make modelling difficult. Consequently, decision-makers consider multiple models that provide different predictions and policy recommendations and then synthesize that information into a policy decision. We use...