Medienecho
Medienecho November 2025 Reint Gropp: Expertendialog: Wege zur Stärkung des Wirtschaftswachstums in Deutschland in: Youtube, 12.11.2025 Steffen Müller: Wir erwarten das höchste…
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Archiv
Medienecho-Archiv 2021 2020 2019 2018 2017 2016 Dezember 2021 IWH: Ausblick auf Wirtschaftsjahr 2022 in Sachsen mit Bezug auf IWH-Prognose zu Ostdeutschland: "Warum Sachsens…
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Alumni
Alumni IWH provides guidance and support in job placement after graduation, including letters of recommendation and career advice. Graduates have found placements in academia…
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6th CompNet Annual Conference
Innovation, firm size, productivity and imbalances in the age of de-globalization 6 th CompNet Annual Conference, June 29-30, 2017, European Commission, Brussels, Belgium As the…
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ProdTalks
CompNet ProdTalks CompNet ProdTalks is a monthly recurring 1.5 hour virtual event, two selected papers will be presented including presentation, discussion and Q&A. The top ic…
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1st FINPRO - Finance and Productivity Conference
1st FINPRO - Finance and Productivity Conference The Great Financial Crisis of 2007/2008 still casts a shadow on many developed economies in terms of real outcomes, such as…
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2nd FINPRO - Finance and Productivity Conference
2nd FINPRO - Finance and Productivity Conference A conference jointly organised by the Competitiveness Research Network (CompNet), the European Bank for Reconstruction and…
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
Nr. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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Is Risk the Fuel of the Business Cycle? Financial Frictions and Oil Market Disturbances
Christoph Schult
IWH Discussion Papers,
Nr. 4,
2024
Abstract
I estimate a dynamic stochastic general equilibrium (DSGE) model for the United States that incorporates oil market shocks and risk shocks working through credit market frictions. The findings of this analysis indicate that risk shocks play a crucial role during the Great Recession and the Dot-Com bubble but not during other economic downturns. Credit market frictions do not amplify persistent oil market shocks. This result holds as long as entry and exit rates of entrepreneurs are independent of the business cycle.
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A Congestion Theory of Unemployment Fluctuations
Yusuf Mercan, Benjamin Schoefer, Petr Sedláček
American Economic Journal: Macroeconomics,
Nr. 1,
2024
Abstract
We propose a theory of unemployment fluctuations in which newhires and incumbentworkers are imperfect substitutes. Hence, attempts to hire away the unemployed during recessions diminish the marginal product of new hires, discouraging job creation. This single feature achieves a ten-fold increase in the volatility of hiring in an otherwise standard search model, produces a realistic Beveridge curve despite countercyclical separations, and explains 30–40% of U.S. unemployment fluctuations. Additionally, it explains the excess procyclicality of new hires’ wages, the cyclical labor wedge, countercyclical earnings losses from job displacement, and the limited steady-state effects of unemployment insurance.
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