@techreport{eprints4081,
           title = {A Neural Network Ensemble Approach for GDP
Forecasting},
            year = {2021},
     institution = {IMT School for Advanced Studies Lucca},
            type = {EIC working paper series},
           month = {March},
          author = {Luigi Longo and Massimo Riccaboni and Armando Rungi},
         address = {Lucca},
        keywords = {Keywords: macroeconomic forecasting; machine learning; neural networks; dynamic factor model; Covid-19 crisis; Mixed frequency.
JEL codes: C53, E37, 051},
        abstract = {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.},
             url = {http://eprints.imtlucca.it/4081/}
}