A Neural Network Ensemble Approach for GDP Forecasting

Luigi Longo, Massimo Riccaboni, Armando Rungi
2021 Social Science Research Network  
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 Generalized 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 component of the ensemble by
more » ... ering 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.
doi:10.2139/ssrn.3894861 fatcat:6x5uetofhvc35iksl2ni6r7agm