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 2019

Katja Heinisch

in: One-off Publications, No. 3, 2019

Abstract

Die deutsche Wirtschaft schrumpfte im zweiten Quartal 2019 um 0,1%, nachdem das Bruttoinlandsprodukt zu Jahresbeginn noch um 0,4% zugelegt hatte. Der aktuelle IWH-Flash-Indikator deutet darauf hin, dass die Konjunktur auch in den kommenden beiden Quartalen schwach bleiben wird. So dürfte die Wirtschaft im dritten Quartal 2019 stagnieren, für das vierte Quartal kann ein kleines Plus von 0,2% erwartet werden.

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

Christoph Schult Katja Heinisch Oliver Holtemöller

in: IWH Discussion Papers, No. 16, 2019

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. Using a multi-region dynamic general equilibrium model, this paper studies potential economic consequences of a coal phase-out in Germany. Different regional phase-out scenarios are simulated with varying timing structures. We find that a politically induced coal phase-out would lead to an increase in the national unemployment rate by about 0.10 percentage points from 2020 to 2040, depending on the specific scenario. The effect on regional unemployment rates varies between 0.18 to 1.07 percentage points in the lignite regions. However, 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 10,000 people by 2040.

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Refereed Publications

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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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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment

Katja Heinisch Rolf Scheufele

in: Empirical Economics, No. 2, 2018

Abstract

In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.

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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.

read publication

cover_DP_2019-16.jpg

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

Christoph Schult Katja Heinisch Oliver Holtemöller

in: IWH Discussion Papers, No. 16, 2019

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. Using a multi-region dynamic general equilibrium model, this paper studies potential economic consequences of a coal phase-out in Germany. Different regional phase-out scenarios are simulated with varying timing structures. We find that a politically induced coal phase-out would lead to an increase in the national unemployment rate by about 0.10 percentage points from 2020 to 2040, depending on the specific scenario. The effect on regional unemployment rates varies between 0.18 to 1.07 percentage points in the lignite regions. However, 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 10,000 people by 2040.

read publication

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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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