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Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model

Author

Listed:
  • Changli He

    (Tianjin University of Finance and Economics)

  • Jian Kang

    (Tianjin University of Finance and Economics)

  • Timo Teräsvirta

    (Aarhus University and CREATES)

  • Shuhua Zhang

    (Tianjin University of Finance and Economics)

Abstract

We consider a vector version of the Shifting Seasonal Mean Autoregressive model. The model is used for describing dynamic behaviour of and contemporaneous dependence between a number of long monthly temperature series for 20 cities in Europe, extending from the second half of the 18th century until mid-2010s. The results indicate strong warming in the winter months, February excluded, and cooling followed by warming during the summer months. Error variances are mostly constant over time, but for many series there is systematic decrease between 1820 and 1850 in April. Error correlations are considered by selecting two small sets of series and modelling correlations within these sets. Some correlations do change over time, but a large majority remains constant. Not surprisingly, the correlations generally decrease with the distance between cities, but geography also plays a role.

Suggested Citation

  • Changli He & Jian Kang & Timo Teräsvirta & Shuhua Zhang, 2019. "Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model," CREATES Research Papers 2019-18, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2019-18
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    References listed on IDEAS

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    Cited by:

    1. Robinson Kruse‐Becher, 2025. "Adaptive Now‐ and Forecasting of Global Temperatures Under Smooth Structural Changes," Environmetrics, John Wiley & Sons, Ltd., vol. 36(6), September.
    2. He, Changli & Kang, Jian & Teräsvirta, Timo & Zhang, Shuhua, 2021. "Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model," Energy Economics, Elsevier, vol. 97(C).
    3. Changli He & Jian Kang & Annastiina Silvennoinen & Timo Teräsvirta, 2025. "The Effect of the North Atlantic Oscillation on Monthly Precipitation in Selected European Locations: A Non‐Linear Time Series Approach," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.

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    JEL classification:

    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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