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Machine Learning and Nowcasts of Swedish GDP

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  • Kristian Jönsson

    (Sveriges Riksbank)

Abstract

The current article investigates if machine learning techniques, more specifically the nearest neighbor algorithm, can be used for nowcasting Swedish GDP growth utilizing business tendency survey data. The results show that the machine learning algorithm can work at least as well as the linear indicator models that have become standard workhorses in Swedish GDP growth nowcasting. This is an indication that nowcasting model suits could benefit from including also machine learning methods going forward.

Suggested Citation

  • Kristian Jönsson, 2020. "Machine Learning and Nowcasts of Swedish GDP," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 123-134, November.
    • Handle: RePEc:spr:jbuscr:v:16:y:2020:i:2:d:10.1007_s41549-020-00049-9
    DOI: 10.1007/s41549-020-00049-9
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    References listed on IDEAS

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    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. Maria Billstam & Kristina Frändén & Johan Samuelsson & Pär Österholm, 2017. "Quasi-Real-Time Data of the Economic Tendency Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 105-138, May.
    3. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    4. Beechey, Meredith & Österholm, Pär, 2010. "Forecasting inflation in an inflation-targeting regime: A role for informative steady-state priors," International Journal of Forecasting, Elsevier, vol. 26(2), pages 248-264, April.
    5. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    8. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.
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    Cited by:

    1. Ramaharo, Franck Maminirina & Rasolofomanana, Gerzhino H, 2023. "Nowcasting Madagascar's real GDP using machine learning algorithms," AfricArxiv vpuac, Center for Open Science.
    2. Kristian Jönsson, 2024. "Neighbor Weighting and Distance Metrics in Nearest Neighbor Nowcasting of Swedish GDP," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(4), pages 1077-1089, December.

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    More about this item

    Keywords

    Nowcasting; Forecasting; Economic tendency survey; Machine learning; GDP;
    All these keywords.

    JEL classification:

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

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