Forecasting national recessions of the United States with state-level climate risks: Evidence from model averaging in Markov-switching models

Oguzhan Cepni, Christina Christou and Rangan Gupta

Economics Letters, 2023, vol. 227, issue C

Abstract: 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.

Keywords: Business fluctuations and cycles; Climate risks; Markov-switching models; Model averaging (search for similar items in EconPapers)
JEL-codes: C22 C53 E32 E37 Q54 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Forecasting National Recessions of the United States with State-Level Climate Risks: Evidence from Model Averaging in Markov-Switching Models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:227:y:2023:i:c:s0165176523001465

DOI: 10.1016/j.econlet.2023.111121

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