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,
No. 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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12.03.2024 • 8/2024
Risk in the banking sector: four out of ten top supervisors come from the financial industry
Europe's banks realise excess returns on the stock market when their alumni join the boards of national supervisory authorities. A study by the Halle Institute for Economic Research (IWH) shows that this happens more frequently than previously recognised. The findings indicate a risk to financial stability and call for a more merit-based, transparent appointment of senior regulators.
Michael Koetter
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