Publikace na Fakulta sociálních věd, Matematicko-fyzikální fakulta, Centrum pro ekonomický výzkum a doktorské studium |
2011
Abstrakt
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.