Forecasting unemployment in the euro area with machine learning

Periklis Gogas, Theophilos Papadimitriou and Emmanouil Sofianos

Journal of Forecasting, 2022, vol. 41, issue 3, 551-566

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.

Date: 2022
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https://doi.org/10.1002/for.2824

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:41:y:2022:i:3:p:551-566

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