Assistant Professor (W1) in Financial Economics
Job Vacancy Assistant Professor (W1) in Financial Economics The Faculty of Economics and Management of Otto von Guericke University Magdeburg (OvGU) in cooperation with the Halle…
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Gender Equality & Anti-Discrimination
Equal Opportunities at IWH IWH commits to actively promoting equal opportunities for men and women, going beyond already existing guidelines. In 2013, 2016, 2019, 2022 and again…
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Privacy
Data Protection Policy We take the protection of your personal data very seriously and treat your personal data with confidentiality and in compliance with the provisions of law…
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Centre for Evidence-based Policy Advice
Centre for Evidence-based Policy Advice (IWH-CEP) The Centre for Evidence-based Policy Advice (IWH-CEP) of the IWH was founded in 2014. It is a platform that bundles and…
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Evaluation of the InvKG and the federal STARK programme
Evaluation of the InvKG and the federal STARK programme Coal Regions Investment Act (InvKG) and the Federal Government’s STARK programme On behalf of the Federal Ministry for…
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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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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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Board Announcements
Board Announcements Apr 27, 2026: Evaluation Report Nov 06, 2025: Evaluation 2025: feedback Nov 03, 2025: Evaluation 2025: important information Oct 02, 2025: Second Trial…
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Scientific Advisory Board
Scientific Advisory Board As an association established and registered under German civil law the IWH is composed of different internal bodies through which it is led and…
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10th Vintage
The CompNet 10th Vintage Dataset 10th Vintage dataset is now available! The CompNet dataset provides a comprehensive set of micro-aggregated indicators, specifically designed to…
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