Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.