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Extreme weather shocks and state-level inflation of the United States

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  • Liao, Wenting
  • Sheng, Xin
  • Gupta, Rangan
  • Karmakar, Sayar

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

  • 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).
    • Handle: RePEc:eee:ecolet:v:238:y:2024:i:c:s0165176524001976
    DOI: 10.1016/j.econlet.2024.111714
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    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).
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    4. 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).
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    10. Sheng, Xin & Marfatia, Hardik A. & Gupta, Rangan & Ji, Qiang, 2023. "The non-linear response of US state-level tradable and non-tradable inflation to oil shocks: The role of oil-dependence," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. 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.
    12. Bhatt, Vipul & Kishor, N Kundan & Ma, Jun, 2017. "The impact of EMU on bond yield convergence: Evidence from a time-varying dynamic factor model," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 206-222.
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    Cited by:

    1. 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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    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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