A neural network ensemble approach for GDP forecasting

DOI10.1016/J.JEDC.2021.104278OpenAlexW3215967556MaRDI QIDQ2115947

Armando Rungi, Luigi Longo, Massimo Riccaboni

Publication date: 15 March 2022

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

Full work available at URL: http://eprints.imtlucca.it/4081/

zbMATH Keywords

neural networksmachine learningdynamic factor modelmacroeconomic forecastingCOVID-19 crisis

Mathematics Subject Classification ID

Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Uses Software

• TensorFlow
• shap

Cites Work

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• Approximately normal tests for equal predictive accuracy in nested models
• Tests of Equality Between Sets of Coefficients in Two Linear Regressions
• The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

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