The European Commission’s growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts’ quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.

This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.

Die deutsche Wirtschaft tritt seit über zwei Jahren auf der Stelle. In den kommenden Quartalen dürfte eine langsame Erholung einsetzen. Aber an den Trend von vor der COVID-19-Pandemie wird das Wirtschaftswachstum auf absehbare Zeit nicht mehr anknüpfen können. Die Dekarbonisierung, die Digitalisierung, der demografische Wandel und wohl auch der stärkere Wettbewerb mit Unternehmen aus China haben strukturelle Anpassungsprozesse in Deutschland ausgelöst, die die Wachstumsaussichten für die deutsche Wirtschaft dämpfen. 

Das Bruttoinlandsprodukt dürfte im Jahr 2024 um 0,1% sinken und in den kommenden beiden Jahren um 0,8% bzw. 1,3% zunehmen. Damit revidieren die Institute ihre Prognose vom Frühjahr 2024 leicht nach unten. Getragen wird die schmalspurige Erholung vom steigenden privaten Verbrauch, der von kräftigen Zuwächsen der real verfügbaren Einkommen angeregt wird. Das Anziehen der Konjunktur in wichtigen Absatzmärkten, wie den europäischen Nachbarländern, wird den deutschen Außenhandel stützen. Zusammen mit günstigeren Finanzierungsbedingungen kommt dies den Anlageinvestitionen zugute. Die Wirtschaftspolitik sollte Produktivitätshemmnisse abbauen, den Strukturwandel zulassen und die politische Unsicherheit verringern.