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
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Publications

Recent Publications

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IWH-Flash-Indikator II. und III. Quartal 2024

Katja Heinisch Oliver Holtemöller Axel Lindner Birgit Schultz

in: IWH Flash Indicator, No. 2, 2024

Abstract

Die deutsche Wirtschaft stagniert nun schon seit zwei Jahren. Auch zu Beginn des Jahres 2024 gab es mit einem leichten Plus von 0,2% keinen konjunkturellen Neustart. Vor allem war die Kauflaune der privaten Haushalte gedrückt, aber auch die Industrie kämpft weiter mit ungünstigen Rahmenbedingungen und einer schwachen Nachfrage. Hohe Zinsen und Energiepreise, aber auch strukturelle Probleme wie bürokratische Restriktionen fordern dabei ihren Tribut, wie nicht zuletzt an den steigenden Insolvenzzahlen zu sehen ist. Im internationalen Umfeld werden die Krisenherde und Spannungen nicht weniger, dennoch ist die Weltkonjunktur weiter aufwärtsgerichtet. Damit steigt bei den deutschen Unternehmen die Hoffnung, im Kielwasser mitziehen zu können. Alles in allem dürfte das Bruttoinlandsprodukt (BIP) laut IWH-Flash-Indikator im zweiten Quartal 2024 um 0,3% und im dritten Quartal 2024 um 0,1% steigen (vgl. Abbildung 1).

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Frühzeitige Ermittlung stabiler Ergebnisse zum Bruttoinlandsprodukt bzw. realen Wirtschaftswachstum und der Bruttowertschöpfung auf Länderebene

Boris Blagov Franziska Exß Katja Heinisch Oliver Holtemöller Clara Krause Torsten Schmidt

in: RWI Projektberichte, 2024

Abstract

In dieser Studie wird geprüft, ob die Genauigkeit der ersten Schätzung der Bruttowertschöpfung und des Bruttoinlandsprodukts für die Bundesländer erhöht und damit das Ausmaß der nachfolgenden Revisionen reduziert werden kann. Dazu werden alternative ökonometrische Methoden und zusätzliche Daten herangezogen. Zunächst wird untersucht, in welchen Bereichen die Revisionen stärker ausfallen als in anderen. Dabei werden das BIP und die Bruttowertschöpfung (BWS) auf der Wirtschaftszweig-Gliederungsebene A*10 mit Zusammenfassungen in die Untersuchung einbezogen. Anschließend werden die amtlichen Ergebnisse mit denen der alternativen Ansätze verglichen. Insgesamt ist das Ausmaß, in dem der Revisionsbedarf mit alternativen Methoden verringert werden konnte, relativ gering. 

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IWH-Flash-Indikator I. und II. Quartal 2024

Katja Heinisch Oliver Holtemöller Axel Lindner Birgit Schultz

in: IWH Flash Indicator, No. 1, 2024

Abstract

Im Jahr 2023 ging das deutsche Bruttoinlandsprodukt um 0,3% gegenüber dem Vorjahr zurück. Während im zweiten und dritten Quartal eine Stagnation der gesamtwirtschaftlichen Produktion zu beobachten war, sank die Wirtschaftsleistung in Deutschland im vierten Quartal 2023 um 0,3%. Insbesondere dürften die gestiegenen Verbraucherpreise dazu beigetragen haben, dass die preisbereinigten Konsumausgaben der privaten Haushalte deutlich zurückgingen. Zusätzlich belastete politische und wirtschaftliche Unsicherheit den Konsum. Der Anstieg der Zinsen wirkte im vergangenen Jahr ebenfalls dämpfend auf die Konjunktur. Die Verunsicherung wird auch in der ersten Jahreshälfte andauern, so dass das Bruttoinlandsprodukt (BIP) laut IWH-Flash-Indikator im ersten Quartal 2024 stagnieren und im zweiten Quartal 2024 lediglich um knapp 0,2% steigen dürfte (vgl. Abbildung 1).

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Refereed Publications

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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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Evidence-based Support for Adaptation Policies in Emerging Economies

Maximilian Banning Anett Großmann Katja Heinisch Frank Hohmann Christian Lutz Christoph Schult

in: Low Carbon Economy, No. 1, 2023

Abstract

Climate change is increasingly evident, and the design of effective climate adaptation policies is important for regional and sectoral economic growth. We propose different modelling approaches to quantify the socio-economic impacts of climate change on three vulnerable countries (Kazakhstan, Georgia, and Vietnam) and design specific adaptations. We use a Dynamic General Equilibrium (DGE) model for Vietnam and an economy-energy-emission (E3) model for the other two countries. Our simulations until 2050 show that selected adaptation measures, in particular in the agricultural sector, have positive implications for GDP. However, some adaptation measures can even increase greenhouse gas emissions. Focusing on GDP alone can lead to welfare-reducing policy decisions.

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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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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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