Investigating long range dependence in temperatures in Siberia
In this paper we examine monthly mean temperatures in 40 selected stations in Siberia for the time period January 1937–December 2020 using long range dependence techniques. In particular, we use a fractionally integrated model that incorporates a linear time trend along with a seasonal structure. Ou...
| Published in: | Polar Science |
|---|---|
| Main Authors: | , |
| Format: | Other/Unknown Material |
| Language: | English |
| Published: |
Polar Science
2022
|
| Subjects: | |
| Online Access: | https://reunir.unir.net/handle/123456789/14546 https://doi.org/10.1016/j.polar.2022.100876 |
| Summary: | In this paper we examine monthly mean temperatures in 40 selected stations in Siberia for the time period January 1937–December 2020 using long range dependence techniques. In particular, we use a fractionally integrated model that incorporates a linear time trend along with a seasonal structure. Our results show first that long memory is present in all stations with significantly positive values for the differencing parameter, though, at the same time the seasonal component seems to be important in all cases. Performing seasonal unit root tests, the results support nonstationary seasonality and working with the seasonal differenced data, the results differ depending on the structure of the error term: if the errors are uncorrelated, long memory is present; however, allowing autocorrelation, this feature disappears in favor of a short memory pattern. |
|---|