%0 Report
%9 EIC working paper series
%A Longo, Luigi
%A Riccaboni, Massimo
%A Rungi, Armando
%C Lucca
%D 2021
%F eprints:4081
%I IMT School for Advanced Studies Lucca
%K Keywords: macroeconomic forecasting; machine learning; neural networks; dynamic factor model; Covid-19 crisis; Mixed frequency.  JEL codes: C53, E37, 051
%N 2
%T A Neural Network Ensemble Approach for GDP  Forecasting
%U http://eprints.imtlucca.it/4081/
%X We propose an ensemble learning methodology to forecast the future US GDP  growth release. Our approach combines a Recurrent Neural Network (RNN) with  a Dynamic Factor model accounting for time-variation in mean with a General-  ized Autoregressive Score (DFM-GAS). The analysis is based on a set of predictors  encompassing a wide range of variables measured at different frequencies. The  forecast exercise is aimed at evaluating the predictive ability of each model's com-  ponent of the ensemble by considering variations in mean, potentially caused by  recessions affecting the economy. Thus, we show how the combination of RNN and  DFM-GAS improves forecasts of the US GDP growth rate in the aftermath of the  2008-09 global financial crisis. We find that a neural network ensemble markedly  reduces the root mean squared error for the short-term forecast horizon.