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).

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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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Stellungnahme „Übergreifende Kostenbetrachtung der Auswirkungen des Klimawandels in Schleswig-Holstein“

Katja Heinisch Oliver Holtemöller Christoph Schult

in: IWH Policy Notes, No. 1, 2023

Abstract

<b>anlässlich der Anhörung im Umwelt- und Agrarausschuss des Schleswig-Holsteinischen Landtags ‒ Antrag der Fraktion der SPD, Drucksache 20/414</b><br /><br />Der Klimawandel in Schleswig-Holstein führt zu Veränderungen in Umwelt, Wirtschaft und Arbeitswelt, und er hat Auswirkungen auf die Gesundheit der Menschen. Der wissenschaftliche Konsens geht davon aus, dass die sozioökonomischen und ökologischen Effekte des Klimawandels weltweit überwiegend negativ sein werden. Aus diesem Grund schreibt das Klimaschutzgesetz vor, dass die deutschen Treibhausgasemissionen bis zum Jahr 2030 um mindestens 65% und bis zum Jahr 2040 um mindestens 88% reduziert werden sollen; bis zum Jahr 2045 soll Klimaneutralität in Deutschland erreicht werden. Schleswig-Holstein hat eigene Klimaschutzziele und Maßnahmen eingeführt. Unsicherheiten bestehen jedoch hinsichtlich der nationalen und regionalen Kosten des Klimawandels. Bisherige Studien deuten darauf hin, dass in der zweiten Hälfte des <br />21. Jahrhunderts in Deutschland die jährlichen Verluste des Bruttoinlandsprodukts unter einem Prozent liegen werden. Zur Plausibilisierung dieser Zahl ist es notwendig, eine transparente und replizierbare Klimawandelfolgenabschätzung für Schleswig-Holstein vorzunehmen. Es wird daher empfohlen, dem Antrag zuzustimmen.

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IWH-Flash-Indikator I. Quartal und II. Quartal 2023

Katja Heinisch Oliver Holtemöller Axel Lindner Birgit Schultz

in: IWH Flash Indicator, No. 1, 2023

Abstract

Im vierten Quartal 2022 ging die Wirtschaftsleistung in Deutschland um 0,2% zurück. Insbesondere die privaten Haushalte erfüllten sich aufgrund der hohen Inflation weniger Konsumwünsche als noch im Quartal zuvor. Die Unterstützung seitens des Staates bei den hohen Energiepreisen federn die gestiegenen Lebenshaltungskosten der privaten Haushalte nur teilweise ab. Diese Kaufkraftverluste werden die Konsumenten wohl noch einige Zeit belasten. Die Unternehmen wurden hingegen bereits von den gesunkenen Beschaffungskosten auf den Weltmärkten etwas entlastet, und auch die Lieferkettenprobleme gingen zuletzt zurück. Allerdings trüben zahlreiche Krisenherde weltweit die Aussichten der deutschen Wirtschaft erneut ein. Zwar kommt es laut IWH-Flash-Indikator im ersten Quartal 2023 zu einer kurzen vorübergehenden Aufhellung, und die deutsche Wirtschaft legt um 0,5% zu. Jedoch schon im zweiten Quartal dürfte sich der Abwärtstrend mit einem Rückgang des Bruttoinlandsprodukts (BIP) um 0,3% fortsetzen (vgl. Abbildung 1).

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The IWH Forecasting Dashboard – From Forecasts to Evaluation and Comparison

Katja Heinisch Christoph Behrens Jörg Döpke Alexander Foltas Ulrich Fritsche Tim Köhler Karsten Müller Johannes Puckelwald Hannes Reichmayr

in: IWH Technical Reports, No. 1, 2023

Abstract

The paper describes the “Halle Institute for Economic Research (IWH) Forecasting Dashboard (ForDas)”. This tool aims at providing, on a non-commercial basis, historical and actual macroeconomic forecast data for the Germany economy to researchers and interested audiences. The database renders it possible to directly compare forecast quality across selected institutions and over time. It is partly based on data collected in the DFG-funded project “Macroeconomic Forecasts in Great Crises”.

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Refereed Publications

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Power Generation and Structural Change: Quantifying Economic Effects of the Coal Phase-out in Germany

Katja Heinisch Oliver Holtemöller Christoph Schult

in: Energy Economics, 2021

Abstract

In the fight against global warming, the reduction of greenhouse gas emissions is a major objective. In particular, a decrease in electricity generation by coal could contribute to reducing CO2 emissions. We study potential economic consequences of a coal phase-out in Germany, using a multi-region dynamic general equilibrium model. Four regional phase-out scenarios before the end of 2040 are simulated. We find that the worst case phase-out scenario would lead to an increase in the aggregate unemployment rate by about 0.13 [0.09 minimum; 0.18 maximum] percentage points from 2020 to 2040. The effect on regional unemployment rates varies between 0.18 [0.13; 0.22] and 1.07 [1.00; 1.13] percentage points in the lignite regions. A faster coal phase-out can lead to a faster recovery. The coal phase-out leads to migration from German lignite regions to German non-lignite regions and reduces the labour force in the lignite regions by 10,100 [6300; 12,300] people by 2040. A coal phase-out until 2035 is not worse in terms of welfare, consumption and employment compared to a coal-exit until 2040.

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(Since when) are East and West German Business Cycles Synchronised?

Stefan Gießler Katja Heinisch Oliver Holtemöller

in: Jahrbücher für Nationalökonomie und Statistik, No. 1, 2021

Abstract

We analyze 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 we 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, synchronisation among East and West German business cycles seems to have become weaker again recently.

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Nowcasting East German GDP Growth: a MIDAS Approach

João Carlos Claudio Katja Heinisch Oliver Holtemöller

in: Empirical Economics, No. 1, 2020

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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Working Papers

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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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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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