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

Changli He, Jian Kang, Annastiina Silvennoinen and Timo Teräsvirta

Journal of Econometrics, 2024, vol. 239, issue 1

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 the precise geographical location also plays a role.

Keywords: Changing seasonality; Nonlinear model; Time-varying correlation; Vector smooth transition autoregression (search for similar items in EconPapers)
JEL-codes: C32 C52 Q54 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:239:y:2024:i:1:s0304407623002105

DOI: 10.1016/j.jeconom.2023.105494

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