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.
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 |
File in questo prodotto:
Eventuali allegati, non sono esposti
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.