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

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  • Magazzino, Cosimo
  • Mele, Marco
  • Mutascu, Mihai

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.

Suggested Citation

  • Magazzino, Cosimo & Mele, Marco & Mutascu, Mihai, 2025. "An artificial neural network experiment on the prediction of the unemployment rate," Journal of Policy Modeling, Elsevier, vol. 47(3), pages 471-491.
    • Handle: RePEc:eee:jpolmo:v:47:y:2025:i:3:p:471-491
    DOI: 10.1016/j.jpolmod.2024.10.004
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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