Forecasting unemployment in the euro area with machine learning

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.

Prediction of Unemployment In India Using Fb Prophet

“…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. [2]Desaling Germay et.al (2016) Unemployment is one of the several socio-economic problems exist in all countries of the world. It affects people's living standard and nations socioeconomic status.…”

Estimation of the unemployment rate in Turkey: A comparison of the ARIMA and machine learning models including Covid-19 pandemic periods

“…They have concluded that the conventional modelling techniques suits better for Spain while ANN and ARIMA based hybrid methods are recommended for other countries. Unemployment in the European Union is estimated using three machine learning methods namely decision trees (DT), random forests (RF), and support vector machines (SVM) in the study of [ 7 ]. The results of their study imply that the optimal RF model outperforms the other models by reaching a forecasting accuracy of 88.5% [ 7 ].…”

“…Unemployment in the European Union is estimated using three machine learning methods namely decision trees (DT), random forests (RF), and support vector machines (SVM) in the study of [ 7 ]. The results of their study imply that the optimal RF model outperforms the other models by reaching a forecasting accuracy of 88.5% [ 7 ]. ARIMA is performed for the Indian youth in a recent study [ 8 ].…”

Forecasting stability and growth pact compliance using machine learning

“…Macroeconomic studies involving machine learning methods are still rare in the literature but are becoming increasingly popular (see e.g. Medeiros et al., 2021; Gogas et al., 2022; Yoon, 2021). The use of machine learning to study SGP compliance is original.…”