Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model

Changli He, Jian Kang, Timo Teräsvirta and Shuhua Zhang

Energy Economics, 2021, vol. 97, issue C

Abstract: The purpose of this paper is to study differences in long monthly Asian and European temperature series. The longest available Asian series are those of Beijing and Shanghai, and they are compared with the ones for St Petersburg, Dublin and Uccle that have a rather different climate. The comparison is carried out in the Vector Shifting Mean and Covariance Autoregressive model that the authors have previously used to analyse 20 long European temperature series. This model gives information about mean shifts in these five temperature series as well as (error) correlations between them. The results suggest, among other things, that warming has begun later in China than in Europe, but that the change in the summer months in both Beijing and Shanghai has been quite rapid.

Keywords: Climate change; Changing seasonality; Long monthly Chinese temperature series; Nonlinear model; Nonlinear time series; Time-varying correlation; Time-varying variance; Time-varying vector smooth transition autoregression (search for similar items in EconPapers)
JEL-codes: C32 C52 Q54 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:97:y:2021:i:c:s0140988321000761

DOI: 10.1016/j.eneco.2021.105171

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