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.