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Macroeconomic Forecasting Using Machine Learning: A Case of Slovakia

Author

Listed:
  • Ádám Csápai

    (University of Economics in Bratislava)

Abstract
We assess the forecasting performance of the selected machine learning methods. According to previous research, they can enhance short-term forecasting performance. We forecast industrial production, inflation and unemployment in Slovakia. We compare the forecasting performance of the models using the mean absolute error and root-mean-squared error. We forecast the variables using ensemble machine learning techniques, such as random forest, bagging and boosting. Additionally, we explore regularized least squares models, such as ridge regression, lasso regression, and elastic net models. Finally, we examine the forecasting performance of neural networks and compare the mean and trimmed mean of model forecasts with individual model performance. Our findings affirm that these methods can enhance forecast accuracy of short-term forecasts, particularly when a relatively large dataset is available. Individual machine learning models prove themselves to be even more accurate than the averages of model forecasts.

Suggested Citation

  • Ádám Csápai, 0000. "Macroeconomic Forecasting Using Machine Learning: A Case of Slovakia," Proceedings of Economics and Finance Conferences 14115967, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iefpro:14115967
    as

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    File URL: https://iises.net/proceedings/international-conference-on-economics-finance-and-business-prague-2023-1/table-of-content/detail?cid=141&iid=005&rid=15967
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    References listed on IDEAS

    as
    1. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    2. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    3. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    4. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    5. Yutaka Kurihara & Akio Fukushima, 2019. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries," Applied Economics and Finance, Redfame publishing, vol. 6(3), pages 1-6, May.
    6. Clements, Michael P. & Hendry, David F. (ed.), 2011. "The Oxford Handbook of Economic Forecasting," OUP Catalogue, Oxford University Press, number 9780195398649.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Economic forecasting; Slovakia; Ensemble machine learning; Regularized least squares; Neural networks;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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