TY - RPRT SN - 2279-6894 A1 - Longo, Luigi A1 - Riccaboni, Massimo A1 - Rungi, Armando N2 - 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. M1 - imt_eic_working_paper KW - Keywords: macroeconomic forecasting; machine learning; neural networks; dynamic factor model; Covid-19 crisis; Mixed frequency. JEL codes: C53 KW - E37 KW - 051 Y1 - 2021/03// CY - Lucca AV - public TI - A Neural Network Ensemble Approach for GDP Forecasting UR - http://eprints.imtlucca.it/4081/ ID - eprints4081 EP - 35 ER -