An artificial neural network experiment on the prediction of the unemployment rate

Cosimo Magazzino, Marco Mele and Mihai Mutascu

Journal of Policy Modeling, 2025, vol. 47, issue 3, 471-491

Abstract: This paper proposes an advanced Artificial Neural Networks (ANN) methodology with a Genetic test in order to estimate unemployment forecasting in 23 high-tech and most-developed countries over the period 1998–2016. The main findings reveal that the methodology adopted ensures an excellent accuracy of unemployment forecasting for the selected countries, allowing the analysis of the contributions of each input to unemployment estimation as well. A significant role is exerted by GDP, labor productivity, population growth, and Artificial Intelligence innovation, while inflation assumes only a secondary role. A minor contribution is also observed in Foreign Direct Investments and government size. Therefore, economic growth based on innovation in Artificial Intelligence with explicit effects on productivity, under adequate population growth, seems to drive the unemployment rate.

Keywords: unemployment forecasting; Artificial Neural Networks; Genetic test; GDP; inflation; labor productivity; population growth (search for similar items in EconPapers)
JEL-codes: C15 E24 O30 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jpolmo:v:47:y:2025:i:3:p:471-491

DOI: 10.1016/j.jpolmod.2024.10.004

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