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Dr Katja Heinisch

Dr Katja Heinisch
Current Position

since 1/13

Head of the Research Group Econometric Tools for Macroeconomic Forecasting and Simulation

Halle Institute for Economic Research (IWH) – Member of the Leibniz Association

since 9/09

Member of the Department Macroeconomics

Halle Institute for Economic Research (IWH) – Member of the Leibniz Association

Research Interests
  • international macroeconomics
  • applied time series econometrics and short-term forecasting
  • macroeconometric modeling

Katja Heinisch joined the Department of Macroeconomics in September 2009. Her research focuses on short-term forecasting and macroeconometric modelling.

Katja Heinisch earned a diploma from Chemnitz University of Technology and University of Strasbourg. She received her PhD from Osnabrück University. Katja Heinisch gained international research experience while working at the European Central Bank (ECB) and the International Monetary Fund (IMF).

Your contact

Dr Katja Heinisch
Dr Katja Heinisch
- Department Macroeconomics
Send Message +49 345 7753-836 LinkedIn profile

Publications

Citations
383

Recent Publications

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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, No. 6, 2025

Abstract

<p>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.</p>

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IWH-Flash-Indikator II. und III. Quartal 2025

Katja Heinisch Oliver Holtemöller Axel Lindner Birgit Schultz

in: IWH Flash Indicator, No. 2, 2025

Abstract

<p>Nach einer Phase weitgehender Stagnation zeigt die deutsche Wirtschaft zu Beginn des Jahres 2025 Anzeichen einer leichten Erholung. Im ersten Quartal stieg das Bruttoinlandsprodukt (BIP) um 0,2% und machte damit den Rückgang aus dem vierten Quartal 2024 wieder wett. Dennoch bleibt die wirtschaftliche Dynamik insgesamt sehr verhalten (vgl. Abbildung 1). Zwar legten sowohl der private Konsum als auch die Investitionen gegenüber dem Vorquartal zu, doch deutet bislang wenig auf eine nachhaltige Trendwende hin.</p> <p>Auch wenn sich die aktuelle Geschäftslage vieler Unternehmen verbessert hat, trüben sich die Erwartungen bereits wieder ein. Neben der Unsicherheit bezüglich der US-Wirtschaftspolitik, die sich dämpfend auf die Investitionsneigung auswirkt, ist derzeit schwer abschätzbar, inwieweit die neue Bundesregierung Impulse setzen kann. Eine schnelle wirtschaftliche Erholung ist nicht zu erwarten. Laut IWH-Flash-Indikator wird das BIP im zweiten Quartal 2025 um 0,3% und im dritten Quartal um 0,2% zulegen.</p>

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Step by Step ‒ A Quarterly Evaluation of EU Commission's GDP Forecasts

Katja Heinisch

in: Journal of Forecasting, No. 3, 2025

Abstract

<p>The European Commission’s growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts’ quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.</p>

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Refereed Publications

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

Katja Heinisch Christoph Schult Carola Stapper

in: Applied Economic Letters, forthcoming

Abstract

<p>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.</p>

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Step by Step ‒ A Quarterly Evaluation of EU Commission's GDP Forecasts

Katja Heinisch

in: Journal of Forecasting, No. 3, 2025

Abstract

<p>The European Commission’s growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts’ quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.</p>

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Conditional Macroeconomic Survey Forecasts: Revisions and Errors

Alexander Glas Katja Heinisch

in: Journal of International Money and Finance, November 2023

Abstract

Using data from the European Central Bank's Survey of Professional Forecasters and ECB/Eurosystem staff projections, we analyze the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the updating and ex-post performance of predictions for inflation, real GDP growth and unemployment are related to beliefs about future oil prices, exchange rates, interest rates and wage growth. While oil price and exchange rate predictions are updated more frequently than macroeconomic forecasts, the opposite is true for interest rate and wage growth expectations. Beliefs about future inflation are closely associated with oil price expectations, whereas expected interest rates are related to predictions of output growth and unemployment. Exchange rate predictions also matter for macroeconomic forecasts, albeit less so than the other variables. With regard to forecast errors, wage growth and GDP growth closely comove, but only during the period when interest rates are at the effective zero lower bound.

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Working Papers

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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, No. 6, 2025

Abstract

<p>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.</p>

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Economic Sentiment: Disentangling Private Information from Public Knowledge

Katja Heinisch Axel Lindner

in: IWH Discussion Papers, No. 15, 2021

Abstract

This paper addresses a general problem with the use of surveys as source of information about the state of an economy: Answers to surveys are highly dependent on information that is publicly available, while only additional information that is not already publicly known has the potential to improve a professional forecast. We propose a simple procedure to disentangle the private information of agents from knowledge that is already publicly known for surveys that ask for general as well as for private prospects. Our results reveal the potential of our proposed technique for the usage of European Commissions‘ consumer surveys for economic forecasting for Germany.

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Conditional Macroeconomic Forecasts: Disagreement, Revisions and Forecast Errors

Alexander Glas Katja Heinisch

in: IWH Discussion Papers, No. 7, 2021

Abstract

Using data from the European Central Bank‘s Survey of Professional Forecasters, we analyse the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the heterogeneity, updating and ex-post performance of predictions for inflation, real GDP growth and the unemployment rate are related to assumptions about future oil prices, exchange rates, interest rates and wage growth. Our findings indicate that inflation forecasts are closely associated with oil price expectations, whereas expected interest rates are used primarily to predict output growth and unemployment. Expectations about exchange rates and wage growth also matter for macroeconomic forecasts, albeit less so than oil prices and interest rates. We show that survey participants can considerably improve forecast accuracy for macroeconomic outcomes by reducing prediction errors for external conditions. Our results contribute to a better understanding of the expectation formation process of experts.

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Topics

Forecasts
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