Although a forecasting model has very good statistical properties and the mean of the residuals equals zero, it can produce systematic errors during a short period. In the case of regular publications, forecasters want to prevent such a persistence of errors over several periods. For this reason, a safeguard model can be used to inform the forecaster when there is a risk that the standard model (i.e. the best specified model on average) leads to persistent errors over several months or quarters.
This paper explains why and how such a safeguard model has been built in order to improve the forecasts of French GDP at the current quarter horizon (nowcasts), which are officially published by the French central bank. The official benchmark model for GDP nowcasts is an aggregated model that relies exclusively on survey in the manufacturing industry. In the long run, this model still has the best performances. On the contrary, the safeguard model is a disaggregated model which features equations for the valued added of 6 sectors. From this example, we provide general remarks on the advantages of disaggregation as well as how such safeguard models can be used in practice.
Camille Thubin, Thomas Ferrière, Éric Monnet, Magali Marx and Vichett Oung
Classification JEL : C52, C53, E37
Keywords : GDP nowcasting; Aggregation; Mixed-frequency data.
Updated on: 06/12/2018 10:59