We propose an ensemble learning methodology to forecast the future US GDP growth release. Our approach combines a Recurrent Neural Network (RNN) and a Dynamic Factor model accounting for time-variation in the mean with a Generalized Autoregressive Score (DFM-GAS). We show how our approach improves forecasts in the aftermath of the 2008-09 global financial crisis by reducing the forecast error for the one-quarter horizon. An exercise on the COVID-19 recession shows a good performance during the economic rebound. Eventually, we provide an interpretable machine learning routine based on integrated gradients to evaluate how the features of the model reflect the evolution of the business cycle.

A neural network ensemble approach for GDP forecasting

Longo, Luigi
;
Riccaboni, Massimo;Rungi, Armando
2021

Abstract

We propose an ensemble learning methodology to forecast the future US GDP growth release. Our approach combines a Recurrent Neural Network (RNN) and a Dynamic Factor model accounting for time-variation in the mean with a Generalized Autoregressive Score (DFM-GAS). We show how our approach improves forecasts in the aftermath of the 2008-09 global financial crisis by reducing the forecast error for the one-quarter horizon. An exercise on the COVID-19 recession shows a good performance during the economic rebound. Eventually, we provide an interpretable machine learning routine based on integrated gradients to evaluate how the features of the model reflect the evolution of the business cycle.
Macroeconomic forecasting, Machine learning, Neural networks, Dynamic factor model, COVID-19 crisis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11771/19679
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