Working Paper: CEPR ID: DP7343
Authors: Maximo Camacho; Gabriel Prezquirs
Abstract: We set out a model to compute short-term forecasts of the euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. With this data set, we show that our simple factor model algorithm, which uses a clear, easy-to-replicate methodology, is able to forecast the euro area GDP growth as well as professional forecasters who can combine the best forecasting tools with the possibility of incorporating their own judgement. In this context, we provide examples showing how data revisions and data availability affect point forecasts and forecast uncertainty.
Keywords: business cycle; forecasting; time series
JEL Codes: C22; E27; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| short-term forecasting model for euro area GDP growth (E17) | GDP growth accuracy (O47) |
| short-term forecasting model for euro area GDP growth (E17) | mean squared errors (C20) |
| model's ability to account for noise in preliminary GDP estimates (E10) | forecasting performance (C53) |
| model's forecasts encompass those of professional forecasters (E17) | professional forecasters' forecasts (F37) |