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

Publikation: Working paperForskning


  • rp19_18

    Forlagets udgivne version, 3,95 MB, PDF-dokument

  • Changli He, Tianjin University of Finance and Economics, Kina
  • Jian Kang, Tianjin University of Finance and Economics, Kina
  • Timo Teräsvirta
  • Shuhua Zhang, Tianjin University of Finance and Economics, Kina
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.
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider77
StatusUdgivet - 1 nov. 2019
SerietitelCREATES Research Papers


  • Changing seasonality, Nonlinear model, Vector smooth transition, Autoregression

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