forecasting

Can machine learning catch the COVID-19 recession?

Based on evidence gathered from a newly built large macroeconomic dataset (MD) for the UK, labelled UK-MD and comparable to similar datasets for the United States and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components).

Macroeconomic modelling at the Institute: hopes, challenges and a lasting contribution

The Institute is a world leader in macroeconomic modelling and forecasting. It has produced quarterly economic forecasts for around sixty years, supported by macroeconomic models. The aim of the original builders of macroeconomic models was to transform understanding of how economies worked and use that knowledge to improve economic policy. In the early years, when computers were rare, macroeconomic modelling was a new frontier and Institute economists were among the first to produce a working model of the UK economy.

Macro Modelling with Many Models

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.