Forecasting economic activity using a neural network in uncertain times: Monte Carlo evidence and application to the German GDP

Oliver Holtemöller and Boris Kozyrev

No 6/2024, IWH Discussion Papers from Halle Institute for Economic Research (IWH)

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.

Keywords: forecasting; neural network; nowcasting; time series models (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:iwhdps:287749

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