Dr. Katja Heinisch

Dr. Katja Heinisch
Aktuelle Position

seit 1/13

Leiterin der Forschungsgruppe Ökonometrische Methoden für wirtschaftliche Prognosen

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 9/09

Wissenschaftliche Mitarbeiterin 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
Mitglied - Abteilung Makroökonomik
Nachricht senden +49 345 7753-836

Publikationen

Neueste Publikationen

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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, im Erscheinen

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.

Publikation lesen

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(Since When) Are East and West German Business Cycles Synchronised?

Stefan Gießler Katja Heinisch Oliver Holtemöller

in: IWH-Diskussionspapiere, Nr. 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.

Publikation lesen

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IWH-Flash-Indikator I. Quartal und II. Quartal 2019

Katja Heinisch

in: Einzelveröffentlichungen, Nr. 1, 2019

Abstract

Die deutsche Wirtschaft stagnierte im vierten Quartal 2018, nachdem das Bruttoinlandsprodukt im dritten Quartal um 0,2% zurückgegangen war. Der aktuelle IWH-Flash-Indikator deutet darauf hin, dass die Wirtschaft auch zu Beginn des Jahres 2019 noch keinen neuen Schwung bekommen hat. Im zweiten Quartal 2019 dürfte dann mit einem kleinen Plus von 0,1% langsam wieder Fahrt aufgenommen werden.

Publikation lesen

 

Referierte Publikationen

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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, im Erscheinen

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.

Publikation lesen

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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, Nr. 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.

Publikation lesen

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

Katja Heinisch Rolf Scheufele

in: Empirical Economics, Nr. 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.

Publikation lesen

Arbeitspapiere

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(Since When) Are East and West German Business Cycles Synchronised?

Stefan Gießler Katja Heinisch Oliver Holtemöller

in: IWH-Diskussionspapiere, Nr. 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.

Publikation lesen

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Outperforming IMF Forecasts by the Use of Leading Indicators

Katja Drechsel Sebastian Giesen Axel Lindner

in: IWH-Diskussionspapiere, Nr. 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.

Publikation lesen

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Should We Trust in Leading Indicators? Evidence from the Recent Recession

Katja Drechsel Rolf Scheufele

in: IWH-Diskussionspapiere, Nr. 10, 2010

Abstract

The paper analyzes leading indicators for GDP and industrial production in Germany. We focus on the performance of single and pooled leading indicators during the pre-crisis and crisis period using various weighting schemes. Pairwise and joint significant tests are used to evaluate single indicator as well as forecast combination methods. In addition, we use an end-of-sample instability test to investigate the stability of forecasting models during the recent financial crisis. We find in general that only a small number of single indicator models were performing well before the crisis. Pooling can substantially increase the reliability of leading indicator forecasts. During the crisis the relative performance of many leading indicator models increased. At short horizons, survey indicators perform best, while at longer horizons financial indicators, such as term spreads and risk spreads, improve relative to the benchmark.

Publikation lesen
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