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

[Zeitschriftenartikel]

Frühwirth-Schnatter, Sylvia
Wagner, Helga

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 ... mehr

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.... weniger

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
https://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)


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.