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EnglishWe describe observation driven time series models for Student-t and EGB2 conditional distributions in which the signal is a linear function of past values of the score of the conditional distribution. These specifications produce models that are easy to implement and deal with outliers by what amounts to a soft form of trimming in the case of t and a soft form of Winsorizing in the case of EGB2. We show how a model with trend and seasonal components can be used as the basis for a seasonal adjustment procedure. The methods are illustrated with US and Spanish data.
| Titolo: | Robust time series models with trend and seasonal components |
| Autore/i: | Caivano, Michele; Harvey, Andrew; LUATI, ALESSANDRA |
| Autore/i Unibo: | |
| Anno: | 2016 |
| Rivista: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/s13209-015-0134-1 |
| Abstract: | We describe observation driven time series models for Student-t and EGB2 conditional distributions in which the signal is a linear function of past values of the score of the conditional distribution. These specifications produce models that are easy to implement and deal with outliers by what amounts to a soft form of trimming in the case of t and a soft form of Winsorizing in the case of EGB2. We show how a model with trend and seasonal components can be used as the basis for a seasonal adjustment procedure. The methods are illustrated with US and Spanish data. |
| Data stato definitivo: | 2016-10-03T16:03:10Z |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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