forecasting, as demonstrated by out-of-sample predictions for 2017. …

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN …

forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep …

In many macroeconomic forecasting applications factor models are used to cope with large datasets. This study aligns … variational autoencoders with macroeconomic factor modeling and proposes an extension to adapt this framework for forecasting … forecasting power. The results suggest significant improvements in the forecasting accuracy of four major US macroeconomic time …

I evaluate whether incorporating sub-national trends improves macroeconomic fore-casting accuracy in a deep machine … forecasting model outperforms equivalent methods based on country-level data and achieves a 0.14 percentage point average error … when forecasting out-of-sample monthly percentage changes in real GDP over a twelve-month horizon. The estimated model …

forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions … the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six …

. We conclude that the forecasting performance of neuro-fuzzy-system in the out-of-sample period is much more superior and …

-time forecasting results based on rolling window prediction methods indicate that multivariate adaptive linear vector autoregression …