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Seasonal generalized AR models.
- Source :
-
Communications in Statistics: Theory & Methods . 2024, Vol. 53 Issue 3, p1065-1080. 16p. - Publication Year :
- 2024
-
Abstract
- This paper looks at a novel type of seasonality labeled as a seasonal Generalized auto-regressive (GAR) model. The seasonal GAR models are found to be short-memory models, and expressions for the autocorrelation function and large sample results for the parameter estimates are established. Traditional Box-Jenkins seasonality models and Gegenbauer seasonality models are compared with the seasonal GAR model. Finally, the three methods are compared in the analysis of a specific process - the Mauna Loa CO2 data - showing that in this case, the seasonal GAR model provides forecasts with a lower mean squared error. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03610926
- Volume :
- 53
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Theory & Methods
- Publication Type :
- Academic Journal
- Accession number :
- 174101524
- Full Text :
- https://doi.org/10.1080/03610926.2022.2100422