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Extreme Weather Shocks and State-Level Inflation of the United States

Author

Listed:
  • Wenting Liao

    (School of Finance, Renmin University of China, Beijing, People's Republic of China)

  • Xin Sheng

    (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, UK)

  • Rangan Gupta

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

  • Sayar Karmakar

    (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA)

Abstract

This study investigates the impact of a metric of extreme weather shocks on 32 state-level inflation rates of the United States (US) over the quarterly period of 1989:01 to 2017:04. In this regard, we first utilize a dynamic factor model with stochastic volatility (DFM-SV) to filter out the national factor from the local components of overall, non-tradable and tradable inflation rates, to ensure that the effect of regional climate risks is not underestimated, given the derived sizeable common component. Second, using impulse responses derived from linear and nonlinear local projections models, we find statistically significant increases in the state (and national) factor of overall inflation rates, with the aggregate effect being driven by the tradable sector relative to the non-tradable one, particularly across the agricultural states in comparison to the non (less)-agricultural ones. Our findings have important policy implications.

Suggested Citation

  • Wenting Liao & Xin Sheng & Rangan Gupta & Sayar Karmakar, 2024. "Extreme Weather Shocks and State-Level Inflation of the United States," Working Papers 202402, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202402
    as

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    References listed on IDEAS

    as
    1. Gupta, Rangan & Nel, Jacobus & Salisu, Afees A. & Ji, Qiang, 2023. "Predictability of economic slowdowns in advanced countries over eight centuries: The role of climate risks," Finance Research Letters, Elsevier, vol. 54(C).
    2. Riccardo Colacito & Bridget Hoffmann & Toan Phan, 2019. "Temperature and Growth: A Panel Analysis of the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(2-3), pages 313-368, March.
    3. Sheng, Xin & Gupta, Rangan & Çepni, Oğuzhan, 2022. "The effects of climate risks on economic activity in a panel of US states: The role of uncertainty," Economics Letters, Elsevier, vol. 213(C).
    4. Niko Hauzenberger & Florian Huber & Gary Koop & Luca Onorante, 2022. "Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1904-1918, October.
    5. Cepni, Oguzhan & Christou, Christina & Gupta, Rangan, 2023. "Forecasting national recessions of the United States with state-level climate risks: Evidence from model averaging in Markov-switching models," Economics Letters, Elsevier, vol. 227(C).
    6. Jonathon Hazell & Juan Herreño & Emi Nakamura & Jón Steinsson, 2022. "The Slope of the Phillips Curve: Evidence from U.S. States," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(3), pages 1299-1344.
    7. Cashin, Paul & Mohaddes, Kamiar & Raissi, Mehdi, 2017. "Fair weather or foul? The macroeconomic effects of El Niño," Journal of International Economics, Elsevier, vol. 106(C), pages 37-54.
    8. Sheng, Xin & Gupta, Rangan & Cepni, Oguzhan, 2022. "Persistence of state-level uncertainty of the United States: The role of climate risks," Economics Letters, Elsevier, vol. 215(C).
    9. Ahmed, M. Iqbal & Cassou, Steven P., 2016. "Does consumer confidence affect durable goods spending during bad and good economic times equally?," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 86-97.
    10. Thanarak Laosuthi & David D. Selover, 2007. "Does El Nino Affect Business Cycles," Eastern Economic Journal, Eastern Economic Association, vol. 33(1), pages 21-42, Winter.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Extreme weather shocks; Inflation; US states; Dynamic factor model with stochastic volatility; Linear and nonlinear local projections; Impulse response functions;
    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
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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