A Neural Network Ensemble Approach for GDP Forecasting

Luigi Longo (), Massimo Riccaboni () and Armando Rungi
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Luigi Longo: IMT School for Advanced Studies Lucca
Massimo Riccaboni: IMT School for Advanced Studies Lucca

No 02/2021, Working Papers from IMT School for Advanced Studies Lucca

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.

Keywords: macroeconomic forecasting; machine learning; neural networks; dynamic factor model; Covid-19 crisis (search for similar items in EconPapers)
JEL-codes: C53 E37 (search for similar items in EconPapers)
Pages: 35
Date: 2021-03, Revised 2021-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-for and nep-mac
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Published in EIC working paper series

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http://eprints.imtlucca.it/4081/ First version, 2021

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Journal Article: A neural network ensemble approach for GDP forecasting (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ial:wpaper:2/2021

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