Abstract
Economic policy makers, international organisations and private-sector forecasters commonly use short-term forecasts of real GDP growth based on monthly indicators, such as industrial production, retail sales and confidence surveys. An assessment of the reliability of such tools and of the source of potential forecast errors is essential. While many studies have evaluated the size of forecast errors related to model specifications and unavailability of data in real time, few have provided a complete assessment of forecast errors, which should notably take into account the impact of data revision. This paper proposes to bridge this gap. Using four years of data vintages for euro area conjunctural indicators, the paper decomposes forecast errors into four elements (model specification, erroneous extrapolations of the monthly indicators, revisions to the monthly indicators and revisions to the GDP data series) and assesses their relative sizes. The results show that gains in accuracy of forecasts achieved by using monthly data on actual activity rather than surveys or financial indicators are offset by the fact that the former set of monthly data is harder to forecast and less timely than the latter set. While the results presented in the paper remain tentative due to limited data availability, they provide a benchmark which future research may build on.
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