Forecasting GDP with Global Components. This Time Is Different

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.