relation: http://eprints.imtlucca.it/4081/
title: A Neural Network Ensemble Approach for GDP  Forecasting
creator: Longo, Luigi
creator: Riccaboni, Massimo
creator: Rungi, Armando
subject: HA Statistics
subject: HB Economic Theory
description: 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.
date: 2021-03
type: Working Paper
type: NonPeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/4081/1/WP_EIC_2_2021.pdf
identifier:   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.