A Bayesian multivariate nonstationary time series model for estimating mutual relationships among variables.
The purpose of this paper is to propose a Bayesian multivariate stochastic model with latent nonstationary trends and seasonal components and show its use to determine the relationships among the variables. The model is expressed in state space form and the parameters of the model are estimated by...
| Published in: | Journal of Econometrics Vol. 75; pp. 147 - 162 |
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| Main Authors: | , , |
| Format: | Article |
| Published: |
Elsevier Science
November 1996
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=512873120&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 512873120 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: November 1996 vid: 75 pid: 1004 pub: Elsevier Science artinfo: ui: 512873120 10.1016/0304-4076(95)01774-7 ppf: 147 ppct: 15 formats: tig: atl: A Bayesian multivariate nonstationary time series model for estimating mutual relationships among variables. aug: au: Kato, Hiroko Naniwa, Sadao Ishiguro, Makio su: Time series analysis Estimation theory Bayesian analysis sug: subj: Time series analysis Estimation theory Bayesian analysis keyword: Prices -- Japan -- Mathematical models ab: The purpose of this paper is to propose a Bayesian multivariate stochastic model with latent nonstationary trends and seasonal components and show its use to determine the relationships among the variables. The model is expressed in state space form and the parameters of the model are estimated by maximum likelihood using a numerical optimization algorithm. The Kalman filter is used to compute the likelihood of the model and the information criterion AIC is used to select the best fitting model. The relationships among variables are examined in the frequency domain using estimated components. Japanese macroeconomic series are analyzed by our procedure. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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