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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Understanding Post-Covid Inflation Dynamics
Martín Harding, Jesper Lindé, Mathias Trabandt
Journal of Monetary Economics,
November
2023
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 heteroskedasticity 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 elevated.
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Medienecho
Medienecho August 2024 Reint Gropp: Zorn und Zaudern in: Handelsblatt, 30.08.2024 Reint Gropp: Warum die Beschäftigung trotz Dauerkrise steigt in: Handelsblatt, 30.08.2024 Reint…
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People Job Market Candidates Doctoral Students PhD Representatives Alumni Supervisors Lecturers Coordinators Job Market Candidates Tommaso Bighelli Job market paper: "The…
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Speed Projects Hier finden Sie die IWH EXplore Speed Projects chronologisch absteigend sortiert. 2022 2021 2020 2019 2018 2017 2016 2015 2014 2022 SPEED 2022/02 Competetiveness in…
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People Job Market Candidates Doctoral Students PhD Representatives Alumni Supervisors Lecturers Coordinators Job Market Candidates Tommaso Bighelli Job market paper: "The…
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The Impact of Delay: Evidence from Formal out-of-Court Restructuring
Stjepan Srhoj, Dejan Kovač, Jacob N. Shapiro, Randall K. Filer
Journal of Corporate Finance,
February
2023
Abstract
Different types of bankruptcy restructuring procedures are used in most legal systems to decide the fate of businesses facing financial hardship. We study how bargaining failures in an under-researched type of restructuring procedure, a formal out-of-the court procedure impacts the economic performance of participating firms. Croatia introduced a “pre-bankruptcy settlement” (PBS) process in the wake of the Great Recession of 2007–2009. A novel dataset provides us with annual financial statements for both sides of more than 180,000 debtor–creditor pairs, enabling us to address selection into failed negotiations by matching a rich set of creditor and debtor characteristics. Failures to settle at the PBS stage due to idiosyncratic bargaining problems, which effectively delay entry into the standard bankruptcy procedure, lead to a lower rate of survival among debtors as well as reduced employment, revenue, and profits. We are the first study to track how bargaining failures diffuse through the network of creditors, finding a significant negative effect on small creditors, but not others. Our results highlight the impact of delay and the importance of structuring bankruptcy procedures, to rapidly resolve uncertainty about firms’ future prospects.
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