Journal article | Zeitschriftenartikel
Stochastic model specification search for Gaussian and partial non-Gaussian state space models
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.
- Extent
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Seite(n): 85-100
- Language
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Englisch
- Notes
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Status: Postprint; begutachtet (peer reviewed)
- Bibliographic citation
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Journal of Econometrics, 154(1)
- Subject
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Wirtschaft
Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
- Event
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Geistige Schöpfung
- (who)
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Frühwirth-Schnatter, Sylvia
Wagner, Helga
- Event
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Veröffentlichung
- (where)
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Niederlande
- (when)
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2009
- DOI
- URN
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urn:nbn:de:0168-ssoar-261769
- Rights
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GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
- Last update
-
21.06.2024, 4:27 PM CEST
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GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln. If you have any questions about the object, please contact the data provider.
Object type
- Zeitschriftenartikel
Associated
- Frühwirth-Schnatter, Sylvia
- Wagner, Helga
Time of origin
- 2009
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Gaussian Model and Additivity
On the structure of Gaussian pricing models and Gaussian Markov functional models
On the structure of Gaussian pricing models and Gaussian Markov functional models
Structured Models with Gaussian Processes
Variational inference for composite Gaussian process models
Safe Learning Control for Gaussian Process Models
Model of rough surfaces with Gaussian processes
Scalable Inference in Latent Gaussian Process Models
Robust Inference for Non-Gaussian SVAR models
Signal transmission in stochastic neuron models with non-white or non-Gaussian noise