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Transfer Learning for Business Cycle Identification

Author

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  • Marcelle Chauvet
  • Rafael R. S. Guimaraes

Abstract

A transfer learning strategy is proposed to identify business cycles phases when data are limited or there is no business cycle dating committee. The approach integrates the idea of storing knowledge gained from one region’s economics experts and applying it to other geographic areas. The first is captured with a supervised deep neural network model, and the second by applying it to another dataset, a domain adaptation procedure. The results indicate the method proposed leads to successful business cycle identification.

Suggested Citation

  • Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    • Handle: RePEc:bcb:wpaper:545
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    File URL: https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/wps545.pdf
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    References listed on IDEAS

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    Cited by:

    1. Hocke, Simone & Klee, Andreas, 2023. "Transformation in der Arbeitswelt gestalten: Welchen Beitrag leistet eine akademische Weiterbildung von Betriebs- und Personalräten?," Working Paper Forschungsförderung 309, Hans-Böckler-Stiftung, Düsseldorf.
    2. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    3. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).

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