Forecasting GDP with Global Components. This Time is Different

Abstract

We examine whether knowledge of in-sample co-movement across countries can be used in a more systematic way to improve forecast accuracy at the national level. In particular, we ask if a model with common international business cycle factors adds marginal predictive power compared to a domestic alternative? To answer this question we use a Dynamic Factor Model (DFM) and run an out-of-sample forecasting experiment. Our results show that exploiting the informational content in a common global business cycle factor improves forecast accuracy in terms of both point and density forecast evaluation across a large panel of countries. We also document that the Great Recession has a huge impact on this result, causing a clear preference shift towards the model including a common global factor. However, this time is different also in other respects. On longer forecasting horizons the performance of the DFM deteriorates substantially in the aftermath of the Great Recession.

Keywords: Bayesian Dynamic Factor Model (BDFM), forecasting, model uncertainty and global factors

JEL Classification: C11, C53, C55, F17