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Machine Learning and Nowcasts of Swedish GDP

Kristian Jönsson ()
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Kristian Jönsson: Sveriges Riksbank

Journal of Business Cycle Research, 2020, vol. 16, issue 2, No 4, 123-134

Abstract: 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.

Keywords: Nowcasting; Forecasting; Economic tendency survey; Machine learning; GDP (search for similar items in EconPapers)
JEL-codes: C53 E27 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s41549-020-00049-9

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