4th and 5th vintage
4th and 5th Vintage CompNet Dataset In 2015-2016 The Competitiveness Research Network released to the public respectively the 4th & 5th Vintage of its aggregated micro-based…
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IWH-CompNet Discussion Papers
IWH-CompNet Discussion Papers This section highlights the CompNet-related paper series , which showcases research in the field of competitiveness—both studies that use CompNet…
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Charts
Info Graphs Sometimes pictures say more than a thousand words. Therefore, we selected a few graphs to present our main topics visually. If you should have any questions or would…
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IWH Research Network in Economics
IWH Research Network in Economics (IWH-ReNEc) The Halle Institute for Economic Research (IWH) conducts its research projects in cooperation with external researchers. The IWH…
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CompNet Database
The CompNet Competitiveness Database The Competitiveness Research Network (CompNet) is a forum for high level research and policy analysis in the areas of competitiveness and…
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Internationalisation
Internationalisation The Leibniz Institute for Economic Research Halle (IWH) is responsible for economic research and economic policy advice on a scientific basis. The institute…
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International Banking Library
International Banking Library The International Banking Library (IBL) is a web-based platform for the exchange of research on cross-border banking. It provides access to data…
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IWH FDI Micro Database
IWH FDI Micro Database The IWH FDI Micro Database (FDI = Foreign Direct Investment) comprises a total population of affiliates of multinational enterprises (MNEs) in selected…
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Reports of the European Forecasting Network (EFN)
Reports of the European Forecasting Network (EFN) The European Forecasting Network (EFN) was a group of macroeconomic experts from different European research institutions (such…
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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,
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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