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Neighbor Weighting and Distance Metrics in Nearest Neighbor Nowcasting of Swedish GDP

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  • Kristian Jönsson

    (Sveriges Riksbank)

Abstract

Forecasting and nowcasting of economic activity can be of great importance in many settings. Business tendency survey data, when employed in a nearest neighbor (NN) algorithm, can produce nowcasts of Swedish GDP that compare well, in terms of predictive performance, to the often-used linear indicator models. The current article probes deeper into the choices available when implementing the nearest neighbor algorithm for nowcasting Swedish GDP and traces out the possible effects on nowcasting accuracy. The dimensions explored include the number of neighbors used for producing the nowcasts, the distance metric employed and distance-weighting of neighbors. The main results indicate the so-called Manhattan distance, or $$L_1$$ L 1 norm, together with equal weighting of 4 or 5 neighbors, could improve nowcasting accuracy for Swedish GDP compared to a setting where a different number of neighbors is used, the $$L_2$$ L 2 or $$L_\infty$$ L ∞ norms are employed and/or distance-based weighting of neighbors is applied.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:jqecon:v:22:y:2024:i:4:d:10.1007_s40953-024-00400-2
    DOI: 10.1007/s40953-024-00400-2
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    5. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    6. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    7. 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.
    8. 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.
    9. 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.
    10. Paolo Fornaro & Henri Luomaranta, 2020. "Nowcasting Finnish real economic activity: a machine learning approach," Empirical Economics, Springer, vol. 58(1), pages 55-71, January.
    11. 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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    More about this item

    Keywords

    Machine learning; Artificial intelligence; Nearest neighbors; Distance metric; Nowcasting; Forecasting; Economic tendency survey; 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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