Publication at Faculty of Social Sciences, Faculty of Mathematics and Physics, Centre for Economic Research and Graduate Education |
2011
Abstract
The authors evaluate the out-of-sample forecasting performance of six competing models at horizons of up to three quarters ahead in a pseudo-real time setup. All the models use information in monthly indicators released ahead of quarterly GDP.
The authors estimate two models - averaged vector autoregressions and bridge equations - relying on just a few monthly indicators. The remaining four models condition the forecast on a large set of monthly series.
These models comprise two standard principal components models, a dynamic factor model based on the Kalman smoother, and a generalized dynamic factor model.