Format:
Online-Ressource
Content:
Abstract: Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series
Note:
Postprint
,
begutachtet (peer reviewed)
,
In: Journal of Econometrics ; 154 (2009) 1 ; 85-100
Language:
English
DOI:
10.1016/j.jeconom.2009.07.003
URN:
urn:nbn:de:0168-ssoar-261769
URL:
https://doi.org/10.1016/j.jeconom.2009.07.003
URL:
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-261769
URL:
https://d-nb.info/1191654710/34
URL:
http://www.ssoar.info/ssoar/handle/document/26176
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