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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
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    References listed on IDEAS

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    Cited by:

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    2. Mehmet Balcilar & Kenny Kutu & Sonali Das & Rangan Gupta, 2025. "Predicting the Conditional Distributions of Inflation and Inflation Uncertainty in South Africa: The Role of Climate Risks," Working Papers 202529, University of Pretoria, Department of Economics.
    3. Martin Bruns & Helmut Lütkepohl, 2026. "Review of Proxy Vector Autoregressive Analysis," Discussion Papers of DIW Berlin 2155, DIW Berlin, German Institute for Economic Research.
    4. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2024. "Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging," Working Papers 202420, University of Pretoria, Department of Economics.

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    Keywords

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    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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