Macro Modelling with Many Models

Abstract

We argue that the next generation of macro modellers at Inflation Targeting central banks should adapt a methodology from the weather forecasting literature known as `ensemble modelling'. In this approach, uncertainty about model specifications (e.g., initial conditions, parameters, and boundary conditions) is explicitly accounted for by constructing ensemble predictive densities from a large number of component models. The components allow the modeller to explore a wide range of uncertainties; and the resulting ensemble `integrates out' these uncertainties using time-varying weights on the components. We provide two examples of this modelling strategy: (i) forecasting inflation with a disaggregate ensemble; and (ii) forecasting inflation with an ensemble DSGE.