A1005
Title: Forecasting economic activity with a neural network in uncertain times: Application to German GDP
Authors: Boris Kozyrev - Halle Institute for Economic Research (IWH) (Germany) [presenting]
Oliver Holtemoeller - Martin Luther University Halle-Wittenberg and Halle Institute for Economic Research (Germany)
Abstract: The forecasting and nowcasting performance of a generalized regression neural network (GRNN) is analyzed. First, evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the true but unknown data-generating process is provided. The analysis shows that GRNN outperforms autoregressive-moving average models in various practical scenarios. An additional check of fitting ARMA using simulated samples is provided. As a result, even though they yield similar to GRNN predictions in many cases, existing ARMA fitting approaches often cannot properly identify a true DGP. Later, GRNN is applied to forecast quarterly German GDP growth, distinguishing between "normal" times and situations with significantly different time-series behaviour, such as during the COVID recession and recovery. The specific data transformation needs to be implemented, i.e., dividing aggregated level values of each indicator by the corresponding GDP value. Then, these ratios are used to perform one-step-ahead forecasting using GRNN. After that, a set of GDP nowcasts is obtained using actual aggregated observations within a given quarter. This algorithm has a high forecasting power, outperforming traditional nowcasting models (AR(1), DFM, model averaging), especially during the COVID-19 crisis.
