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Dr. Katja Heinisch

Dr. Katja Heinisch
Aktuelle Position

seit 1/13

Leiterin der Forschungsgruppe Ökonometrische Methoden für wirtschaftliche Prognosen und Simulationen

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 9/09

Mitglied der Abteilung Makroökonomik

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

Forschungsschwerpunkte
  • internationale Makroökonomik
  • angewandte Zeitreihenökonometrie, insb. Kurzfristprognose
  • strukturelle makroökonometrische Modelle

Katja Heinisch ist seit September 2009 wissenschaftliche Mitarbeiterin in der Abteilung Makroökonomik. Zu ihren Forschungsschwerpunkten zählen insbesondere Kurzfristprognosen und die ökonometrische Modellierung gesamtwirtschaftlicher Zusammenhänge.

Katja Heinisch studierte an der Technischen Universität Chemnitz und der Universität Straßburg. Sie promovierte an der Universität Osnabrück. Während ihrer Dissertationszeit absolvierte Katja Heinisch Forschungsaufenthalte an der Europäischen Zentralbank (EZB) und beim Internationalen Währungsfonds (IWF).

Ihr Kontakt

Dr. Katja Heinisch
Dr. Katja Heinisch
- Abteilung Makroökonomik
Nachricht senden +49 345 7753-836 LinkedIn Profil

Publikationen

Zitationen
383

Neueste Publikationen

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

Katja Heinisch

in: Journal of Forecasting, im Erscheinen

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>

Publikation lesen

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

Katja Heinisch Christoph Schult Carola Stapper

in: Applied Economic Letters, im Erscheinen

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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Begleitende Evaluierung des Investitionsgesetzes Kohleregionen (InvKG) und des STARK-Bundesprogramms ‒ Zweiter Zwischenbericht vom 31.10.2024

Matthias Brachert Katja Heinisch Oliver Holtemöller Florian Kirsch Uwe Neumann Michael Rothgang Torsten Schmidt Christoph Schult Anna Solms Mirko Titze

in: IWH Studies, Nr. 1, 2025

Abstract

<p><strong>Gutachten im Auftrag des Bundesministeriums für Wirtschaft und Klimaschutz</strong></p> <p>Das Klimaschutzgesetz (KSG) sieht eine Reduktion der deutschen Treibhausgasemissionen bis zum Jahr 2030 um 65 Prozent gegenüber den Emissionen im Jahr 1990 vor. Der Ausstieg aus der thermischen Verwertung der Kohle (vor allem der Braunkohle) leistet einen substanziellen Beitrag zum Erreichen dieser Ziele. Der Kohleausstieg stellt die Braunkohlereviere (und die Standorte der Steinkohlekraftwerke) jedoch vor strukturpolitische Herausforderungen. Um den Strukturwandel in diesen Regionen aktiv zu gestalten, hat der Bundestag im August 2020 mit Zustimmung des Bundesrats das Strukturstärkungsgesetz Kohleregionen (StStG) beschlossen. Über dieses Gesetz stellt der Bund bis zum Jahr 2038 Finanzhilfen von 41,09 Mrd. Euro zur Verfügung. Im Fokus der Politikmaßnahmen stehen verschiedene Ziele, vor allem gesamtwirtschaftliche (Wertschöpfung, Wachstum, Steueraufkommen), wettbewerbliche (Produktivität), arbeitsmarktpolitische (Beschäftigung, Beschäftigungsstrukturen), verteilungspolitische (regionale Disparitäten) sowie klimapolitische (Treibhausgasreduzierung, Nachhaltigkeit). Die im StStG vorgesehenen strukturpolitischen Interventionen umfassen ein breites Maßnahmenbündel. Das Gesetz fordert eine begleitende wissenschaftliche Evaluierung des Gesetzes. Bei dem vorliegenden Bericht handelt es sich um das zweite Dokument in diesem Evaluierungszyklus. Der erste Bericht liegt seit Juni 2023 vor und präsentierte ein erstes Lagebild nach dem Start der im Rahmen des Investitionsgesetzes Kohleregionen (InvKG) und des STARK-Bundesprogramms geplanten Maßnahmen. Nachdem nunmehr zahlreiche Maßnahmen in die Umsetzung gehen, nimmt der Strukturwandel an Fahrt auf. Der aktuelle Bericht nimmt eine Aktualisierung vor und erweitert Aussagen zu deren möglichen Effekten. Auch für diesen Bericht bleibt zu berücksichtigen, dass viele der geplanten Maßnahmen noch nicht oder gerade erst begonnen haben, was bei einer fast zwanzigjährigen Laufzeit des Programms durchaus naheliegend ist. Die in diesem Bericht vorgelegten empirischen Analysen basieren auf dem Datenstand vom 30.06.2024, also fast vier Jahre nach Programmstart.</p>

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Referierte Publikationen

cover_journal-of-forecasting.gif

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

Katja Heinisch

in: Journal of Forecasting, im Erscheinen

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>

Publikation lesen

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

Katja Heinisch Christoph Schult Carola Stapper

in: Applied Economic Letters, im Erscheinen

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>

Publikation lesen
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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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Arbeitspapiere

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Economic Sentiment: Disentangling Private Information from Public Knowledge

Katja Heinisch Axel Lindner

in: IWH Discussion Papers, Nr. 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, Nr. 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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How Forecast Accuracy Depends on Conditioning Assumptions

Carola Engelke Katja Heinisch Christoph Schult

in: IWH Discussion Papers, Nr. 18, 2019

Abstract

This paper examines the extent to which errors in economic forecasts are driven by initial assumptions that prove to be incorrect ex post. Therefore, we construct a new data set comprising an unbalanced panel of annual forecasts from different institutions forecasting German GDP and the underlying assumptions. We explicitly control for different forecast horizons to proxy the information available at the release date. Over 75% of squared errors of the GDP forecast comove with the squared errors in their underlying assumptions. The root mean squared forecast error for GDP in our regression sample of 1.52% could be reduced to 1.13% by setting all assumption errors to zero. This implies that the accuracy of the assumptions is of great importance and that forecasters should reveal the framework of their assumptions in order to obtain useful policy recommendations based on economic forecasts.

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