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

Author

Listed:
  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark)

  • Christina Christou

    (School of Economics and Management, Open University of Cyprus, 2252, Latsia, Cyprus)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

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 its (realized) volatility. We find that forecasts obtained from the DMA combination scheme provide timely updates of the US business cycles based on the information content of the metrics of state-level climate risks, particularly volatility of temperature, relative to the corresponding small-scale MS benchmarks that use national-level values of climate change-related predictors.

Suggested Citation

  • Oguzhan Cepni & Christina Christou & Rangan Gupta, 2022. "Forecasting National Recessions of the United States with State-Level Climate Risks: Evidence from Model Averaging in Markov-Switching Models," Working Papers 202252, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202252
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    References listed on IDEAS

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    2. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2015. "Forecasting National Recessions Using State‐Level Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(5), pages 847-866, August.
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    10. Haroon Mumtaz & Laura Sunder‐Plassmann & Angeliki Theophilopoulou, 2018. "The State‐Level Impact of Uncertainty Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(8), pages 1879-1899, December.
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    Cited by:

    1. Liao, Wenting & Sheng, Xin & Gupta, Rangan & Karmakar, Sayar, 2024. "Extreme weather shocks and state-level inflation of the United States," Economics Letters, Elsevier, vol. 238(C).
    2. Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024. "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    3. Xin Sheng & Rangan Gupta & Oguzhan Cepni, 2023. "Time-Varying Effects of Extreme Weather Shocks on Output Growth of the United States," Working Papers 202324, University of Pretoria, Department of Economics.

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    More about this item

    Keywords

    Business fluctuations and cycles; Climate risks; Markov-switching models; Model averaging;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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