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Decoding the Puzzle of Joblessness: Machine Learning Predicts Unemployment Trends in the Americas

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  • Diego Vallarino

    (Independent Researcher, Atlanta, USA)

Abstract

This study investigates the efficacy of diverse machine learning survival models, including Cox, Kernel SVM, DeepSurv, Survival Random Forest, and MTLR models, employing the concordance index to assess their predictive abilities. The primary objective of this research is to identify the most accurate model for forecasting the time it takes for a country to witness a 10% surge in unemployment within a 120-month timeframe (2013-2022), utilizing variables from the MVI dataset of 28 American countries. Through the comparative evaluation of complex survival models, we discovered that DeepSurv, a sophisticated machine learning algorithm, excels in capturing intricate nonlinear relationships, while conventional models exhibit comparable performance under specific circumstances. The weight matrix, a pivotal element of our analysis, meticulously assesses the economic repercussions of various risk factors, vulnerabilities, and capabilities.

Suggested Citation

  • Diego Vallarino, 2025. "Decoding the Puzzle of Joblessness: Machine Learning Predicts Unemployment Trends in the Americas," Journal of Regional Economics, Anser Press, vol. 4(1), pages 1-18, March.
    • Handle: RePEc:bba:j00009:v:4:y:2025:i:1:p:1-18:d:444
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    References listed on IDEAS

    as
    1. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
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