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Dr. Christoph Schult

Dr. Christoph Schult
Aktuelle Position

seit 7/16

Wissenschaftlicher Mitarbeiter der Abteilung Makroökonomik

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

Forschungsschwerpunkte
  • dynamische Makroökonomie
  • Energiemärkte

Christoph Schult ist seit Juli 2016 wissenschaftlicher Mitarbeiter in der Abteilung Makroökonomik. Er forscht zu den Themen dynamische Makroökonomie, Prognosen und Energiemärkte.

Christoph Schult studierte an der Martin-Luther-Universität Halle-Wittenberg und der Humboldt-Universität zu Berlin. Er promovierte 2021.

Ihr Kontakt

Dr. Christoph Schult
Dr. Christoph Schult
- Abteilung Makroökonomik
Nachricht senden +49 345 7753-806

Publikationen

Zitationen
285

Neueste Publikationen

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The Effects of the Iberian Exception Mechanism on Wholesale Electricity Prices and Consumer Inflation: A Synthetic-controls Approach

Miguel Haro Ruiz Christoph Schult Christoph Wunder

in: Applied Economic Letters, im Erscheinen

Abstract

This study employs synthetic control methods to estimate the effect of the Iberian exception mechanism on wholesale electricity prices and consumer inflation, for both Spain and Portugal. We find that the intervention led to an average reduction of approximately 40% in the spot price of electricity between July 2022 and June 2023 in both Spain and Portugal. Regarding overall inflation, we observe notable differences between the two countries. In Spain, the intervention has an immediate effect, and results in an average decrease of 3.5 percentage points over the twelve months under consideration. In Portugal, however, the impact is small and generally close to zero. Different electricity market structures in each country are a plausible explanation.

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Transparency and Forecasting: The Impact of Conditioning Assumptions on Forecast Accuracy

Katja Heinisch Christoph Schult Carola Stapper

in: Applied Economic Letters, im Erscheinen

Abstract

This study investigates the impact of inaccurate assumptions on economic forecast precision. We construct a new dataset comprising an unbalanced panel of annual German GDP forecasts from various institutions, taking into account their underlying assumptions. We explicitly control for different forecast horizons to reflect the information available at the time of release. Our analysis reveals that approximately 75% of the variation in squared forecast errors can be attributed to the variation in squared errors of the initial assumptions. This finding emphasizes the importance of accurate assumptions in economic forecasting and suggests that forecasters should transparently disclose their assumptions to enhance the usefulness of their forecasts in shaping effective policy recommendations.

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Growth Clubs and Regional Economic Convergence in Germany

Oliver Holtemöller Christoph Schult Anna Solms

in: IWH Discussion Papers, Nr. 4, 2026

Abstract

Many countries and regions remain below the level of economic activity of the world’s most advanced economies. Some countries form growth clubs, some are stuck in the middle-income trap, and some stay on a very low level of economic activity. Although this situation is well documented on the country level, there is less evidence at the sub-national level within countries. We estimate county-level capital stocks and price indices and provide a comprehensive county-level data set for Germany. We find no evidence of convergence across all counties even if we condition on important drivers of long-term growth such as physical and human capital accumulation. Instead, we identify five convergence clubs, using endogenous clustering. We analyze differences in growth paths and describe the identified clusters based on variations in contributions of capital, labor, and total factor productivity to economic growth. Additionally, we examine the role of migration for regional development and find that net migration has in particular contributed to growth in richer regions.

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Arbeitspapiere

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Growth Clubs and Regional Economic Convergence in Germany

Oliver Holtemöller Christoph Schult Anna Solms

in: IWH Discussion Papers, Nr. 4, 2026

Abstract

Many countries and regions remain below the level of economic activity of the world’s most advanced economies. Some countries form growth clubs, some are stuck in the middle-income trap, and some stay on a very low level of economic activity. Although this situation is well documented on the country level, there is less evidence at the sub-national level within countries. We estimate county-level capital stocks and price indices and provide a comprehensive county-level data set for Germany. We find no evidence of convergence across all counties even if we condition on important drivers of long-term growth such as physical and human capital accumulation. Instead, we identify five convergence clubs, using endogenous clustering. We analyze differences in growth paths and describe the identified clusters based on variations in contributions of capital, labor, and total factor productivity to economic growth. Additionally, we examine the role of migration for regional development and find that net migration has in particular contributed to growth in richer regions.

Publikation lesen
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Climate Change Economics in Vietnam: Redefining Economic Impact

Christian Otto Christoph Schult Thomas Vogt

in: IWH Discussion Papers, Nr. 15, 2025

Abstract

Vietnam, a lower-middle-income economy, faces severe climate risks from heat waves, sea-level rise, and tropical cyclones, which are expected to intensify under ongoing global warming. Using a dynamic general equilibrium model, we analyze economic transition dynamics from 2015 to 2100, incorporating heat-induced labor productivity losses, agricultural land loss, and cyclone-related property damage. We compare a Paris-compatible scenario limiting warming to below 2 °C with a high-emission scenario reaching 4–5 °C. While output and investment impacts remain highly uncertain and statistically indistinguishable across scenarios until 2100, consumption losses are significantly larger under high emissions, mainly driven by heat-related productivity declines, with cyclones contributing most to uncertainty. These findings underscore the importance of considering multiple impact channels beyond output damages in climate-development research.

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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

in: IWH Discussion Papers, Nr. 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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