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

Economist in the Department of 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).

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

Publications

Recent Publications

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IWH-Flash-Indikator III. Quartal und IV. Quartal 2020

Katja Heinisch Oliver Holtemöller Axel Lindner Birgit Schultz

in: One-off Publications, No. 3, 2020

Abstract

Die Corona-Pandemie hat die deutsche Wirtschaft im Frühjahr 2020 in eine tiefe Rezession gerissen. Das Bruttoinlandsprodukt sank im zwei­ten Quartal 2020 um 10,1%, nach einem Rückgang von 2,0% im Quartal zuvor. Dieser massive Wirtschaftseinbruch war insbesondere den Lockdown-Maßnahmen geschuldet, die das öffentliche und wirtschaft­liche Leben zeitweise auf ein Minimum reduzierten. Seit Anfang Mai wurden die Restriktionen zur Eindämmung der Pandemie gelockert, und die wirtschaftlichen Aktivitäten haben wieder deutlich zugenom­men. Der Tiefpunkt der Rezession ist also durchschritten, allerdings dürfte die Rückkehr zum Vorkrisenniveau auch aufgrund der wieder höheren Fallzahlen und der damit verbundenen Unsicherheit noch länger auf sich warten lassen. Die Wirtschaft dürfte im dritten Quartal 2020 um 4,6% und im vierten Quartal dann um 4,0% expandieren. (vgl. Abbildung).

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IWH-Flash-Indikator II. Quartal und III. Quartal 2020

Katja Heinisch Oliver Holtemöller Axel Lindner Birgit Schultz

in: One-off Publications, No. 2, 2020

Abstract

Die Corona-Pandemie hat dazu geführt, dass das deutsche Bruttoinlandsprodukt im ersten Quartal 2020 um 2,2% im Vergleich zum Vorquartal gesunken ist. Dieser starke Rückgang ist vor allem auf die im Laufe des Monats März in Deutschland und in anderen Ländern eingeführten Maßnahmen zur Eindämmung des Virus zurückzuführen. Bereits aus anderen Gründen war das Bruttoinlandsprodukt schon im vierten Quartal 2019 leicht zurückgegangen. Die Rezession wird sich im laufenden Quartal noch weiter vertiefen und das Bruttoinlandsprodukt um 7,2% zurückgehen, weil bis Mitte Mai die Restriktionen zur Eindämmung der Pandemie noch gravierend waren, aber auch weil private Haushalte und Unternehmen eine Vielzahl von wirtschaftlichen Aktivitäten individuell eingeschränkt haben. Im dritten Quartal 2020 dürfte die Produktion dann wieder zulegen, sofern die Eindämmungsmaßnahmen weiter gelockert werden können (vgl. Abbildung 1).

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IWH-Flash-Indikator I. Quartal und II. Quartal 2020

Katja Heinisch Oliver Holtemöller Axel Lindner Birgit Schultz

in: One-off Publications, No. 1, 2020

Abstract

Das Bruttoinlandsprodukt in Deutschland hat im vierten Quartal, so wie vom IWH-Flash-Indikator im November angezeigt, lediglich stagniert. Für das erste und das zweite Quartal 2020 deutet der IWH-Flash-Indikator wieder auf eine Zunahme des Bruttoinlandsprodukts hin (vgl. Abbildung 1). Allerdings gehen in den Indikator keine Daten ein, die der chinesischen Corona-Epidemie Rechnung tragen.

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Refereed Publications

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Nowcasting East German GDP Growth: a MIDAS Approach

João Carlos Claudio Katja Heinisch Oliver Holtemöller

in: Empirical Economics, forthcoming

Abstract

Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.

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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

Katja Heinisch Rolf Scheufele

in: German Economic Review, forthcoming

Abstract

In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.

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For How Long Do IMF Forecasts of World Economic Growth Stay Up-to-date?

Katja Heinisch Axel Lindner

in: Applied Economics Letters, No. 3, 2019

Abstract

This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.

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Working Papers

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How Forecast Accuracy Depends on Conditioning Assumptions

Carola Engelke Katja Heinisch Christoph Schult

in: IWH Discussion Papers, No. 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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(Since When) Are East and West German Business Cycles Synchronised?

Stefan Gießler Katja Heinisch Oliver Holtemöller

in: IWH Discussion Papers, No. 7, 2019

Abstract

This paper analyses whether and since when East and West German business cycles are synchronised. We investigate real GDP, unemployment rates and survey data as business cycle indicators and employ several empirical methods. Overall, we find that the regional business cycles have synchronised over time. GDP-based indicators and survey data show a higher degree of synchronisation than the indicators based on unemployment rates. However, recently synchronisation among East and West German business cycles seems to become weaker, in line with international evidence.

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Outperforming IMF Forecasts by the Use of Leading Indicators

Katja Drechsel Sebastian Giesen Axel Lindner

in: IWH Discussion Papers, No. 4, 2014

Abstract

This study analyzes the performance of the IMF World Economic Outlook forecasts for world output and the aggregates of both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current and the next year, we examine whether IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time dataset for GDP and for the indicators we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts if the publication of the Outlook is only a few months old.

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