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on Forecasting |
| By: | Holtemöller, Oliver; Kozyrev, Boris |
| 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 |
| JEL: | C22 C45 C53 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:287749&r=for |
| By: | Mr. Sakai Ando |
| Abstract: | How to make forecasts that (1) satisfy constraints, like accounting identities, and (2) are smooth over time? Solving this common forecasting problem manually is resource-intensive, but the existing literature provides little guidance on how to achieve both objectives. This paper proposes a new method to smooth mixed-frequency multivariate time series subject to constraints by integrating the minimum-trace reconciliation and Hodrick-Prescott filter. With linear constraints, the method has a closed-form solution, convenient for a high-dimensional environment. Three examples show that the proposed method can reproduce the smoothness of professional forecasts subject to various constraints and slightly improve forecast performance. |
| Keywords: | Smoothness; Forecast Reconciliation; Minimum Trace Reconciliation; Hodrick-Prescott filter; Cross-sectional; Temporal |
| Date: | 2024–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/066&r=for |