Halle Institute for Economic Research (IWH) – Member of the Leibniz Association
Recovery under structural stress – fiscal policy on slippery ground The recovery of the German economy, although stronger than expected in spring, remains modest. In their joint…
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Macroeconomic Reports
Macroeconomic Reports Local and global: IWH regularly provides current economic data – be it about the state of the East German economy, the macroeconomic development in Germany…
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Board Announcements
Board Announcements Sep 09, 2026: Election results and resilience of the institute Apr 27, 2026: Evaluation Report Nov 06, 2025: Evaluation 2025: feedback Nov 03, 2025: Evaluation…
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IWH Forecasting Dashboard
IWH Forecasting Dashboard The objective of the IWH Forecasting Dashboard (ForDas) is to provide a platform for macroeconomic forecasts from various institutions for the German…
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Green Transition
Green Transition Research and Policy Advice for Structural Change in the German Economy Dossier, 18.06.2024 Green Transition The green transition is a key topic of our time. In a…
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Alumni
IWH Alumni The IWH maintains contact with its former employees worldwide. We involve our alumni in our work and keep them informed, for example, with a newsletter. We also plan…
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Projects
Our Projects 04.2025 ‐ 03.2030 The Role of Multinational Enterprises in the Global Financial Cycle (Programme for Women Professors) Leibniz-Gemeinschaft Professorin Xiang Li,…
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Ludwig (Interview)
About the CIA and a glass of red wine ... Professor Dr Udo Ludwig on the beginnings of our institute The core of the IWH founding team came from the Institute for Applied Economic…
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Research Clusters
Three Research Clusters Each IWH research group is assigned to a topic-oriented research cluster. The clusters are not separate organisational units, but rather bundle the…
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Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach
Katja Heinisch, Fabio Scaramella, Christoph Schult
IWH Discussion Papers,
No. 6,
2025
Abstract
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.
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