Stochastic model specification search for Gaussian and partial non-Gaussian state space models

Übernahme per Copy & Paste
Bibtex-Export
Endnote-Export

[Zeitschriftenartikel]

Frühwirth-Schnatter, Sylvia; Wagner, Helga

(1909 KByte)

Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):http://nbn-resolving.de/urn:nbn:de:0168-ssoar-261769

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.
Klassifikation	Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Freie Schlagwörter	Auxiliary mixture sampling; Bayesian econometrics; Noncentered parameterization; Markov chain Monte Carlo; Variable selection
Sprache Dokument	Englisch
Publikationsjahr	2009
Seitenangabe	S. 85-100
Zeitschriftentitel	Journal of Econometrics, 154 (2009) 1
DOI	http://dx.doi.org/10.1016/j.jeconom.2009.07.003
Status	Postprint; begutachtet (peer reviewed)
Lizenz	PEER Licence Agreement (applicable only to documents from PEER project)