Forecasting National Recessions of the United States with State-level Climate Risks: Evidence from Model Averaging in Markov-switching Models

Oguzhan Cepni, Christina Christou*, Rangan Gupta

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


This paper utilizes Bayesian (static) model averaging (BMA) and dynamic model averaging (DMA) incorporated into Markov-switching (MS) models to forecast business cycle turning points of the United States (US) with state-level climate risks data, proxied by temperature changes and their (realized) volatility. We find that forecasts obtained from the DMA combination scheme provide timely updates of US business cycles based on the information content of metrics of state-level climate risks, particularly the volatility of temperature, relative to the corresponding small-scale MS benchmarks that use national-level values of climate change-related predictors.
Original languageEnglish
Article number111121
JournalEconomics Letters
Number of pages6
Publication statusPublished - Jun 2023


  • Business fluctuations and cycles
  • Climate risks
  • Markov-switching models
  • Model averaging

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