Econometric Tools for Macroeconomic Forecasting and Simulation
The aim of this research group is to enhance research on, and development, implementation, evaluation, and application of quantitative macroeconometric models for forecasting and analysing aggregate economic fluctuations and developments. Research in this group contributes to the econometric foundation and the methodological improvements of the IWH forecasts. During the last years, the IWH has highly specialised in macroeconomic modelling, both for flash estimates and medium-term projections. Furthermore, this group conducts comprehensive empirical analysis and develops econometric tools that are used for third-party funded projects. In the last years, particular models have been developed for e.g. Volkswagen Financial Services AG and for GIZ. The research group contributed in particular on macroeconomic modelling for ministries in Kyrgyzstan and Tajikistan as well as for the institute of forecasting and macroeconomic research (IFMR) Uzbekistan.
IWH Data Project: IWH Real-time Database
Research Cluster
Macroeconomic Dynamics and StabilityYour contact

Mitglied - Department Macroeconomics
EXTERNAL FUNDING
10.2019 ‐ 01.2023
Climate Resilient Economic Development
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
Climate change has a substantial impact on economic growth and a country’s development. This increases the need for reliable and viable approaches to assessing the impact of climate risks and potential adaptation scenarios. Political decision-makers in ministries of planning and economy need sound forecasts in order to design and finance adequate economic policy instruments and actively to take countermeasures. In the pilot countries (Georgia, Kazakhstan and Vietnam), climate risk is included in macroeconomic modelling, enabling the results to be integrated into the policy process so as to facilitate adapted economic planning. The IWH team is responsible for macroeconomic modelling in Vietnam.
05.2020 ‐ 09.2023
ENTRANCES: Energy Transitions from Coal and Carbon: Effects on Societies
European Commission
ENTRANCES aims at examining the effects of the coal phase-out in Europe. How does the phase-out transform society – and what can politics do about it?
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883947.
01.2018 ‐ 12.2023
EuropeAid (EU Framework Contract)
European Commission
07.2016 ‐ 12.2018
Climate Protection and Coal Phaseout: Political Strategies and Measures up to 2030 and beyond
Umweltbundesamt (UBA)
01.2017 ‐ 12.2017
Support to Sustainable Economic Development in Selected Regions of Uzbekistan
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
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
01.2016 ‐ 12.2017
Development of analytical tools based on Input-Output table
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
The aim of the project was the development of an analytical tool to assess the gains and losses of possible state programs supporting the development of the private sector of the Tajik economy.
11.2015 ‐ 12.2016
Employment and Development in the Republic of Uzbekistan
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
Support to sustainable economic development in selected regions of Uzbekistan
05.2016 ‐ 05.2016
Framework and Finance for Private Sector Development in Tajikistan
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
02.2016 ‐ 04.2016
Macroeconomic Reforms and Green Growth - Assessment of economic modelling capacity in Vietnam
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
Refereed Publications

For How Long Do IMF Forecasts of World Economic Growth Stay Up-to-date?
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.

On DSGE Models
in: Journal of Economic Perspectives, No. 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.

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

The European Refugee Crisis and the Natural Rate of Output
in: Applied Economics Letters, No. 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.

Impulse Response Analysis in a Misspecified DSGE Model: A Comparison of Full and Limited Information Techniques
in: Applied Economics Letters, No. 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.
Working Papers

Understanding Post-Covid Inflation Dynamics
in: Working Paper, 2022
Abstract
We propose a macroeconomic model with a nonlinear Phillips curve that has a flat slope when inflationary pressures are subdued and steepens when inflationary pressures are elevated. The nonlinear Phillips curve in our model arises due to a quasi-kinked demand schedule for goods produced by firms. Our model can jointly account for the modest decline in inflation during the Great Recession and the surge in inflation during the Post-Covid period. Because our model implies a stronger transmission of shocks when inflation is high, it generates conditional heteroscedasticity in inflation and inflation risk. Hence, our model can generate more sizeable inflation surges due to cost-push and demand shocks than a standard linearized model. Finally, our model implies that the central bank faces a more severe trade-off between inflation and output stabilization when inflation is high.

Economic Sentiment: Disentangling Private Information from Public Knowledge
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.

Conditional Macroeconomic Forecasts: Disagreement, Revisions and Forecast Errors
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.

Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks
in: IWH Discussion Papers, No. 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.

Integrated Assessment of Epidemic and Economic Dynamics
in: IWH Discussion Papers, No. 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.