Forecasting economic activity using a neural network in uncertain times : Monte Carlo evidence and application to the German GDP / Oliver Holtemöller, Boris Kozyrev ; editor: Halle Institute for Economic Research (IWH) - Member of the Leibniz Association
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Forecasting economic activity using a neural network in uncertain times
Discovery
1884081185
URN
urn:nbn:de:gbv:3:2-1054615
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Publication history
Halle (Saale), Germany : Halle Institute for Economic Research (IWH) - Member of the Leibniz Association, March 2024
Content
1 Online-Ressource (III, 28 Seiten, 1,07 MB) : Diagramme
Edition
Language
eng
Comment
Literaturverzeichnis: Seite 24-26
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.
Series
IWH-Diskussionspapiere ; 2024, no. 6 (March 2024) ppn:837399270