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Forecasting unemployment in the euro area with machine learning

Author

Listed:
  • Periklis Gogas
  • Theophilos Papadimitriou
  • Emmanouil Sofianos

Abstract

Unemployment has a direct impact on public finances and yields serious sociopolitical implications. This study aims to directionally forecast the euro‐area unemployment rate. To the best of our knowledge, no other studies forecast the euro‐area unemployment rate as a whole. The data set includes the unemployment rate and 36 explanatory variables, as suggested by theory and the relevant literature, spanning the period from 1998:4 to 2019:9 in monthly frequency. These variables are fed to three machine learning methodologies: decision trees (DT), random forests (RF), and support vector machines (SVM), while an elastic‐net logistic regression (logit) model is used from the area of econometrics. The results show that the optimal RF model outperforms the other models by reaching a full‐dataset forecasting accuracy of 88.5% and 85.4% on the out‐of‐sample.

Suggested Citation

  • Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:3:p:551-566
    DOI: 10.1002/for.2824
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    References listed on IDEAS

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    3. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
    4. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
    5. Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    6. Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    7. Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.
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    9. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
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