Logo eprints

A Neural Network Ensemble Approach for GDP Forecasting

Longo, Luigi and Riccaboni, Massimo and Rungi, Armando A Neural Network Ensemble Approach for GDP Forecasting. EIC working paper series #2/2021 ISSN 2279-6894.

[img] PDF - Submitted Version
Available under License Creative Commons Attribution Non-commercial.

Download (1MB)


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.

Item Type: Working Paper (EIC working paper series)
Uncontrolled Keywords: Keywords: macroeconomic forecasting; machine learning; neural networks; dynamic factor model; Covid-19 crisis; Mixed frequency. JEL codes: C53, E37, 051
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Research Area: Economics and Institutional Change
Depositing User: Caterina Tangheroni
Date Deposited: 17 Mar 2021 10:04
Last Modified: 17 Mar 2021 10:04
URI: http://eprints.imtlucca.it/id/eprint/4081

Actions (login required)

Edit Item Edit Item