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.

Die Ankündigungen und Entscheidungen der neuen US-Regierung um den Russland-Ukraine-Konflikt und die Zollpolitik haben weltweit zu hoher Unsicherheit geführt. Im Euroraum bleibt die Konjunktur schwach. Auch die deutsche Konjunktur ist weiter im Abschwung. Das Bruttoinlandsprodukt dürfte im Jahr 2025 um 0,1% und im Jahr darauf um 1,3% zunehmen.

Zur Jahreswende dürfte die weltweite Produktion weiterhin in etwa so schnell wie in der Dekade vor der Pandemie expandieren. Die Konjunktur im Euroraum ist nur verhalten, und die Stagnation der deutschen Wirtschaft setzt sich fort. Die Industrie verliert an internationaler Wettbewerbsfähigkeit. Unternehmen und Verbraucher halten sich aufgrund unklarer wirtschaftspolitischer Aussichten mit ihren Ausgaben zurück. Das Bruttoinlandsprodukt dürfte im Jahr 2024 um 0,2% sinken und im Jahr 2025 um 0,4% expandieren.

In this paper, we discuss how environmental damage and emission reduction policies affect the conduct of monetary policy in a two-sector (clean and dirty) dynamic stochastic general equilibrium model. In particular, we examine the optimal response of the interest rate to changes in sectoral inflation due to standard supply shocks, conditional on a given environmental policy. We then compare the performance of a nonstandard monetary rule with sectoral inflation targets to that of a standard Taylor rule. Our main results are as follows: first, the optimal monetary policy is affected by the existence of environmental policy (carbon taxation), as this introduces a distortion in the relative price level between the clean and dirty sectors. Second, compared with a standard Taylor rule targeting aggregate inflation, a monetary policy rule with asymmetric responses to sector-specific inflation allows for reduced volatility in the inflation gap, output gap, and emissions. Third, a nonstandard monetary policy rule allows for a higher level of welfare, so the two goals of welfare maximization and emission minimization can be aligned.