CREATES

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

Research output: Working paper/Preprint Working paperResearch

Standard

Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model. / He, Changli; Kang, Jian; Teräsvirta, Timo; Zhang, Shuhua.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2019. p. 1-77.

Research output: Working paper/Preprint Working paperResearch

Harvard

He, C, Kang, J, Teräsvirta, T & Zhang, S 2019 'Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model' Institut for Økonomi, Aarhus Universitet, Aarhus, pp. 1-77.

APA

He, C., Kang, J., Teräsvirta, T., & Zhang, S. (2019). Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model. (pp. 1-77). Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2019-18

CBE

He C, Kang J, Teräsvirta T, Zhang S. 2019. Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model. Aarhus: Institut for Økonomi, Aarhus Universitet. pp. 1-77.

MLA

He, Changli et al. Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model. 1-77. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2019-18). 2019, 77 p.

Vancouver

He C, Kang J, Teräsvirta T, Zhang S. Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model. Aarhus: Institut for Økonomi, Aarhus Universitet. 2019 Nov 1, p. 1-77.

Author

He, Changli ; Kang, Jian ; Teräsvirta, Timo ; Zhang, Shuhua. / Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model. Aarhus : Institut for Økonomi, Aarhus Universitet, 2019. pp. 1-77 (CREATES Research Papers; No. 2019-18).

Bibtex

@techreport{33d5b8c1e7aa4a459b4dd4f893df7bdb,
title = "Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model",
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.",
keywords = "Changing seasonality, Nonlinear model, Vector smooth transition, Autoregression",
author = "Changli He and Jian Kang and Timo Ter{\"a}svirta and Shuhua Zhang",
year = "2019",
month = nov,
day = "1",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2019-18",
pages = "1--77",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model

AU - He, Changli

AU - Kang, Jian

AU - Teräsvirta, Timo

AU - Zhang, Shuhua

PY - 2019/11/1

Y1 - 2019/11/1

N2 - 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.

AB - 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.

KW - Changing seasonality

KW - Nonlinear model

KW - Vector smooth transition

KW - Autoregression

M3 - Working paper

T3 - CREATES Research Papers

SP - 1

EP - 77

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

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -