Forecasts that minimize mean squared forecast error (MSE) often exhibit excessive volatility, limiting their practical applicability. We address this accuracy-smoothness trade-off by introducing a Multivariate Smooth Sign Accuracy (M-SSA) framework, which extracts smoothed components from leading indicators to enhance the signal-to-noise ratio and control the forecast volatility and timing. Applied to quarterly German GDP growth, our method yields smoothed forecasts that can improve forecasting accuracy, particularly over medium-term horizons. We find that while smoother forecasts tend to lag slightly around turning points, this can be offset by adjusting the forecast horizon. These findings highlight the practicality of the M-SSA framework for both forecasters and policymakers, especially in settings where forecast revisions or policy adjustments are costly.

Der Beitrag untersucht die mittelfristige Entwicklung der deutschen Wirtschaft und der öffentlichen Finanzen vor dem Hintergrund der seit 2025 geltenden neuen EU-Fiskalregeln und der jüngsten Lockerung der nationalen Schuldenbremse. Im Mittelpunkt steht die Frage, ob und unter welchen Bedingungen Deutschland die europäischen Vorgaben zu Defizit, Schuldenstand und Nettoprimärausgaben einhalten kann.

Die Projektgruppe Gemeinschaftsdiagnose prognostiziert für das Jahr 2025 einen leichten Zuwachs des Bruttoinlandsprodukts in Deutschland um 0,2 %. Im weiteren Prognosezeitraum stimuliert die expansive Finanzpolitik die Konjunktur. Die Institute prognostizieren für die kommenden beiden Jahre Expansionsraten von 1,3 % und 1,4 %. Strukturelle Probleme wie abnehmende Wettbewerbsfähigkeit und der demografische Wandel bleiben bestehen. Um Wachstumsperspektiven für die deutsche Wirtschaft zu schaffen, bedarf es einer umfangreichen Reformpolitik. Zur Orientierung präsentieren die Institute einen Zwölf-Punkte-Kompass für den Herbst der Reformen.

Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as a valid instrument to address endogeneity. Using double/debiased machine learning (DML) techniques and partial linear instrumental variable (PLIV) models, we analyze GDP growth forecasts for Germany, conditioning on key exogenous variables such as oil price, exchange rate, and world trade. We find that traditional ordinary least squares (OLS) techniques systematically underestimate the influence of assumption errors, particularly with respect to world trade, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities in the data. However, the effect of oil price assumption errors on GDP forecast errors remains ambiguous. These results underscore the importance of advanced econometric tools to improve the evaluation of macroeconomic forecasts.

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.

Das Produktionspotenzial der deutschen Wirtschaft wächst mittelfristig (2023 bis 2029) mit einer jahresdurchschnittlichen Rate von 0,3% und damit deutlich schwächer als in den Jahren zuvor. Dies ist auf eine ungünstigere Entwicklung aller drei Faktoren (Arbeitsvolumen, Kapitalstock, totale Faktorproduktivität) zurückzuführen. Das potenzielle Wachstum wird insbesondere durch den Rückgang der durchschnittlichen Arbeitszeit gedämpft.

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.