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Monitoring recessions: A Bayesian sequential quickest detection method

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  • Li, Haixi
  • Sheng, Xuguang Simon
  • Yang, Jingyun

Abstract

Monitoring business cycles faces two potentially conflicting objectives: accuracy and timeliness. To strike a balance between these dual objectives, we propose a Bayesian sequential quickest detection method to identify turning points in real time with a sequential stopping time as a solution. Using four monthly indexes of real economic activity in the United States, we evaluated the method’s real-time ability to date the past five recessions. The proposed method identified similar turning-point dates as the National Bureau of Economic Research (NBER), with no false alarms, but on average, it dated peaks four months faster and troughs 10 months faster relative to the NBER announcement. The timeliness of our method is also notable compared to the dynamic factor Markov-switching model: the average lead time was about five months when dating peaks and two months when dating troughs.

Suggested Citation

  • Li, Haixi & Sheng, Xuguang Simon & Yang, Jingyun, 2021. "Monitoring recessions: A Bayesian sequential quickest detection method," International Journal of Forecasting, Elsevier, vol. 37(2), pages 500-510.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:2:p:500-510
    DOI: 10.1016/j.ijforecast.2020.06.013
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    References listed on IDEAS

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    Cited by:

    1. Seulki Chung, 2023. "Real-time Prediction of the Great Recession and the Covid-19 Recession," Papers 2310.08536, arXiv.org, revised May 2024.

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