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

Research output: Working paper/Preprint Working paperResearch


  • rp19_18

    Final published version, 3.95 MB, PDF document

  • Changli He, Tianjin University of Finance and Economics, China
  • Jian Kang, Tianjin University of Finance and Economics, China
  • Timo Teräsvirta
  • Shuhua Zhang, Tianjin University of Finance and Economics, China
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.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages77
Publication statusPublished - 1 Nov 2019
SeriesCREATES Research Papers

See relations at Aarhus University Citationformats

Download statistics

No data available

ID: 170314996