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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

    as
    1. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    2. D. Grubb & R. Jackman & R. Layard, 1982. "Causes of the Current Stagflation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(5), pages 707-730.
    3. D'Amuri, Francesco & Marcucci, Juri, 2009. "‘Google it!’ Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
    4. Farmer, Roger E.A., 2010. "How to reduce unemployment: A new policy proposal," Journal of Monetary Economics, Elsevier, vol. 57(5), pages 557-572, July.
    5. Papadamou, Stephanos & Siriopoulos, Costas, 2009. "Corporate Yield Spread and Real Activity in Emerging Asia: Evidence of a Financial Accelerator for Korea," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 24, pages 275-293.
    6. Shen, Chung-Hua, 1996. "Forecasting macroeconomic variables using data of different periodicities," International Journal of Forecasting, Elsevier, vol. 12(2), pages 269-282, June.
    7. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Georgios Sermpinis & Charalampos Stasinakis & Konstantinos Theofilatos & Andreas Karathanasopoul, 2014. "Inflation and Unemployment Forecasting with Genetic Support Vector Regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 471-487, September.
    8. Thomas R. Cook & Aaron Smalter Hall, 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper RWP 17-11, Federal Reserve Bank of Kansas City.
    9. Gogas, Periklis & Papadimitriou, Theophilos & Sofianos, Emmanouil, 2019. "Money Neutrality, Monetary Aggregates and Machine Learning," DUTH Research Papers in Economics 4-2016, Democritus University of Thrace, Department of Economics.
    10. Roger E. A. Farmer, 2015. "The Stock Market Crash Really Did Cause the Great Recession," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(5), pages 617-633, October.
    11. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    12. Sibande, Xolani & Gupta, Rangan & Wohar, Mark E., 2019. "Time-varying causal relationship between stock market and unemployment in the United Kingdom: Historical evidence from 1855 to 2017," Journal of Multinational Financial Management, Elsevier, vol. 49(C), pages 81-88.
    13. Bharat Barot, 2004. "How accurate are the Swedish forecasters on GDB-Growth, CPI-inflation and unemployment? (1993 - 2001)," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 47(2), pages 249-278.
    14. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    15. Anastasios Evgenidis & Dionisis Philippas & Costas Siriopoulos, 2019. "Heterogeneous effects in the international transmission of the US monetary policy: a factor-augmented VAR perspective," Empirical Economics, Springer, vol. 56(5), pages 1549-1579, May.
    16. Chen, Sophia & Ranciere, Romain, 2019. "Financial information and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1160-1174.
    17. D’Amuri, Francesco & Marcucci, Juri, 2010. "“Google it!” Forecasting the US Unemployment Rate with a Google Job Search index," Global Challenges Papers 60680, Fondazione Eni Enrico Mattei (FEEM).
    18. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    19. repec:iab:iabjlr:v:53:i:1:p:art.3 is not listed on IDEAS
    20. Brunner, Karl & Cukierman, Alex & Meltzer, Allan H., 1980. "Stagflation, persistent unemployment and the permanence of economic shocks," Journal of Monetary Economics, Elsevier, vol. 6(4), pages 467-492, October.
    21. Floros, Ch., 2005. "Forecasting the UK Unemployment Rate: Model Comparisons," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(4), pages 57-72.
    22. Pan, Wei-Fong, 2018. "Does the stock market really cause unemployment? A cross-country analysis," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 34-43.
    23. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    24. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    2. Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
    3. 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.
    4. 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.
    5. 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.
    6. repec:hal:journl:hal-04675599 is not listed on IDEAS
    7. 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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