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On the Evolution of U.S. Temperature Dynamics

Author

Listed:
  • Francis X. Diebold

    (Department of Economics, University of Pennsylvania)

  • Glenn D. Rudebusch

    (Federal Reserve Bank of San Francisco)

Abstract

Climate change is a multidimensional shift. While much research has documented rising mean temperature levels, we also examine range-based measures of daily temperature volatility. Specifically, using data for select U.S. cities over the past half-century, we compare the evolving time series dynamics of the average temperature level, AVG, and the diurnal temperature range, DTR (the difference between the daily maximum and minimum temperatures at a given location). We characterize trend and seasonality in these two series using linear models with time-varying coecients. These straightforward yet flexible approximations provide evidence of evolving DTR seasonality, stable AVG seasonality, and conditionally Gaussian but heteroskedastic innovations for both DTR and AVG.

Suggested Citation

  • Francis X. Diebold & Glenn D. Rudebusch, 2019. "On the Evolution of U.S. Temperature Dynamics," PIER Working Paper Archive 19-012, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:19-012
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    References listed on IDEAS

    as
    1. David Wigglesworth, 2019. "Crop Production and Climate Change: The Importance of Temperature Variability," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 47(4), pages 529-531, December.
    2. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    3. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    4. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
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    Cited by:

    1. Kumar, Naveen & Maiti, Dibyendu, 2025. "Climate change, state capacity and uneven growth: A disaggregated analysis of India," Economic Modelling, Elsevier, vol. 153(C).
    2. María Dolores Gadea Rivas & Jesús Gonzalo, 2022. "A tale of three cities: climate heterogeneity," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 475-511, May.
    3. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2021. "A tale of three cities: climate heterogeneity (special issue of SERIES in homage to Juan J. Dolado)," UC3M Working papers. Economics 32200, Universidad Carlos III de Madrid. Departamento de Economía.

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    Keywords

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    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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