Ökonometrische Methoden für wirtschaftliche Prognosen und Simulationen

Der Forschungsschwerpunkt der Forschungsgruppe liegt in der Entwicklung ökonometrischer Methoden für Kurzfristprognosen (Reduzierte-Form-Modelle), für Regionalisierung und für Langfristprojektionen sowie für strukturelle Prognose- und Simulationsmodelle (DSGE-Modelle). Ferner erstellt sie ökonometrische Hintergrundanalysen für die Prognosetätigkeit der Forschungsgruppe Makroökonomische Analysen und Prognosen. Im Rahmen von Drittmittelprojekten wurden verschiedene makroökonomische Modelle, bspw. für die Volkswagen Financial Services AG oder im Rahmen von GIZ-Projekten für die Wirtschaftsministerien in Kirgistan und Tadschikistan sowie das Institut für makroökonomische Prognosen und Forschung (IFMR) in Usbekistan entwickelt.

IWH-Datenprojekt: IWH Real-time Database

Forschungscluster
Gesamtwirtschaftliche Dynamik und Stabilität

Ihr Kontakt

Dr. Katja Heinisch
Dr. Katja Heinisch
Mitglied - Abteilung Makroökonomik
Nachricht senden +49 345 7753-836

PROJEKTE

10.2019 ‐ 06.2022

An Klimawandel angepasste Wirtschaftsentwicklung

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Der Klimawandel wirkt sich stark auf das Wirtschaftswachstum und die Entwicklung eines Landes aus. Das erhöht den Bedarf an verlässlichen und realisierbaren Ansätzen, mit denen die Auswirkungen von Klimarisiken und potenzielle Anpassungsszenarien bewertet werden können. Die politischen Entscheidungsträger*innen in den Planungs- und Wirtschaftsministerien benötigen fundierte Prognosen, um entsprechende wirtschaftspolitische Instrumente zu konzipieren, zu finanzieren und aktiv gegenzusteuern. In den Pilotländern Kasachstan, Vietnam und Georgien werden Klimarisiken bei der makroökonomischen Modellierung berücksichtigt. Die Ergebnisse werden so in den Politikprozess integriert, dass angepasste Wirtschaftsplanungen entstehen können. Das IWH-Team ist verantwortlich für die makroökonomische Modellierung in Vietnam.

GIZ-Projektseite ansehen

Dr. Katja Heinisch

05.2020 ‐ 04.2023

ENTRANCES: Energy Transitions from Coal and Carbon: Effects on Societies

Europäische Kommission

Ziel von ENTRANCES ist es, die Folgen des Kohleausstiegs in Europa zu untersuchen. Wie verändert der Kohleausstieg die Gesellschaft – und wie kann Politik darauf reagieren?

Projektseite ansehen

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883947.

Professor Dr. Oliver Holtemöller
Dr. Katja Heinisch

01.2018 ‐ 12.2023

EuropeAid (EU-Rahmenvertrag)

Europäische Kommission

Professor Dr. Oliver Holtemöller

07.2016 ‐ 12.2018

Klimaschutz und Kohleausstieg: Politische Strategien und Maßnahmen bis 2030 und darüber hinaus

Umweltbundesamt (UBA)

Dr. Katja Heinisch

01.2017 ‐ 12.2017

Unterstützung einer nachhaltigen Wirtschaftsentwicklung in ausgewählten Regionen Usbekistans

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr. Andrej Drygalla

01.2017 ‐ 12.2017

Short-term Macroeconomic Forecasting Model in Ministry of Economic Development and Trade of Ukraine

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr. Andrej Drygalla

01.2016 ‐ 12.2017

Entwicklung eines analytischen Tools basierend auf einer Input-Output-Tabelle

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Das Ziel des Projektes war die Entwicklung eines Exceltools zur Wirkungsanalyse von Politikmaßnahmen in Tadschikistan basierend auf dem statischen Input-Output-Ansatz.

Dr. Katja Heinisch

11.2015 ‐ 12.2016

Beschäftigung und Entwicklung in der Republik Usbekistan

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Förderung einer nachhaltigen wirtschaftlichen Entwicklung in ausgewählten Regionen Usbekistans

Dr. Katja Heinisch

05.2016 ‐ 05.2016

Rahmenbedingungen und Finanzierungsmöglichkeiten für die Entwicklung des Privatsektors in Tadschikistan

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr. Katja Heinisch

02.2016 ‐ 04.2016

Makroökonomische Reformen und umwelt- und sozialverträgliches Wachstum in Vietnam

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr. Katja Heinisch

Referierte Publikationen

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On DSGE Models

Lawrence J. Christiano Martin S. Eichenbaum Mathias Trabandt

in: Journal of Economic Perspectives, Nr. 3, 2018

Abstract

The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers—like most other economists and policymakers—failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks.

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

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The European Refugee Crisis and the Natural Rate of Output

Katja Heinisch Klaus Wohlrabe

in: Applied Economics Letters, Nr. 16, 2017

Abstract

The European Commission follows a harmonized approach for calculating structural (potential) output for EU member states that takes into account labour as an important ingredient. This article shows how the recent huge migrants’ inflow to Europe affects trend output. Due to the fact that the immigrants immediately increase the working population but effectively do not enter the labour market, we illustrate that the potential output is potentially upward biased without any corrections. Taking Germany as an example, we find that the average medium-term potential growth rate is lower if the migration flow is modelled adequately compared to results based on the unadjusted European Commission procedure.

Publikation lesen

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Impulse Response Analysis in a Misspecified DSGE Model: A Comparison of Full and Limited Information Techniques

Sebastian Giesen Rolf Scheufele

in: Applied Economics Letters, Nr. 3, 2016

Abstract

In this article, we examine the effect of estimation biases – introduced by model misspecification – on the impulse responses analysis for dynamic stochastic general equilibrium (DSGE) models. Thereby, we use full and limited information estimators to estimate a misspecified DSGE model and calculate impulse response functions (IRFs) based on the estimated structural parameters. It turns out that IRFs based on full information techniques can be unreliable under misspecification.

Publikation lesen

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Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models

Sebastian Giesen Rolf Scheufele

in: Journal of Macroeconomics, June 2016

Abstract

In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameter estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.

Publikation lesen

Arbeitspapiere

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Conditional Macroeconomic Forecasts: Disagreement, Revisions and Forecast Errors

Alexander Glas Katja Heinisch

in: IWH Discussion Papers, Nr. 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.

Publikation lesen

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Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks

Annika Camehl Malte Rieth

in: IWH Discussion Papers, Nr. 2, 2021

Abstract

We study the dynamic impact of Covid-19, economic mobility, and containment policy shocks. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through sign and zero restrictions. Incidence and mobility shocks raise cases and deaths significantly for two months. Restrictive policy shocks lower mobility immediately, cases after one week, and deaths after three weeks. Non-pharmaceutical interventions explain half of the variation in mobility, cases, and deaths worldwide. These flattened the pandemic curve, while deepening the global mobility recession. The policy tradeoff is 1 p.p. less mobility per day for 9% fewer deaths after two months.

Publikation lesen

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Epidemics in the Neoclassical and New Keynesian Models

Martin S. Eichenbaum Sergio Rebelo Mathias Trabandt

in: NBER Working Paper, Nr. 27430, 2020

Abstract

We analyze the effects of an epidemic in three standard macroeconomic models. We find that the neoclassical model does not rationalize the positive comovement of consumption and investment observed in recessions associated with an epidemic. Introducing monopolistic competition into the neoclassical model remedies this shortcoming even when prices are completely flexible. Finally, sticky prices lead to a larger recession but do not fundamentally alter the predictions of the monopolistic competition model.

Publikation lesen

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Integrated Assessment of Epidemic and Economic Dynamics

Oliver Holtemöller

in: IWH Discussion Papers, Nr. 4, 2020

Abstract

In this paper, a simple integrated model for the joint assessment of epidemic and economic dynamics is developed. The model can be used to discuss mitigation policies like shutdown and testing. Since epidemics cause output losses due to a reduced labor force, temporarily reducing economic activity in order to prevent future losses can be welfare enhancing. Mitigation policies help to keep the number of people requiring intensive medical care below the capacity of the health system. The optimal policy is a mixture of temporary partial shutdown and intensive testing and isolation of infectious persons for an extended period of time.

Publikation lesen

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How Forecast Accuracy Depends on Conditioning Assumptions

Carola Engelke Katja Heinisch Christoph Schult

in: IWH Discussion Papers, Nr. 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.

Publikation lesen
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