Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/287749 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
IWH Discussion Papers No. 6/2024
Verlag: 
Halle Institute for Economic Research (IWH), Halle (Saale)
Zusammenfassung: 
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.
Schlagwörter: 
forecasting
neural network
nowcasting
time series models
JEL: 
C22
C45
C53
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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