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'Rust in peace': Why are Germany’s bridges and schools falling apart?Oliver HoltemöllerThe Guardian, June 3, 2025
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.
We study the aggregate, distributional, and welfare effects of fiscal policy responses to Germany’s energy crisis using a novel Ten-Agents New-Keynesian (TENK) model. The energy crisis, compounded by the COVID-19 pandemic, led to sharp increases in energy prices, inflation, and significant consumption disparities across households. Our model, calibrated to Germany’s income and consumption distribution, evaluates key policy interventions, including untargeted and targeted transfers, a value-added tax cut, energy tax reductions, and an energy cost brake. We find that untargeted transfers had the largest short-term aggregate impact, while targeted transfers were most cost-effective in supporting lower-income households. Other instruments, as the prominent energy cost brake, yielded comparably limited welfare gains. These results highlight the importance of targeted fiscal measures in addressing distributional effects and stabilizing consumption during economic crises.
Im Koalitionsvertrag von CDU, CSU und SPD vom 7. Februar 2018 formuliert die neue Bundesregierung ihre rentenpolitischen Ziele. Diese sind vor dem Hintergrund der Bevölkerungsdynamik in Deutschland zu sehen. Ab dem Jahr 2020 wird sich die Altersstruktur der deutschen Bevölkerung deutlich verändern. In diesem Beitrag werden Simulationsrechnungen zu den Konsequenzen der rentenpolitischen Maßnahmen aus dem Koalitionsvertrag für die Finanzierung der gesetzlichen Rentenversicherung mit Hilfe eines Simulationsmodells dargestellt. Die im Koalitionsvertrag vorgesehenen Leistungsausweitungen verursachen langfristig Kosten in Höhe von etwa 2½ Prozentpunkten beim Beitragssatz zur gesetzlichen Rentenversicherung. Es werden ferner Maßnahmen – auch im Vergleich zu den Rentensystemen anderer Länder – diskutiert, mit denen der Anstieg des Beitragssatzes begrenzt werden könnte.
