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dc.contributor.authorChen, CWSen_US
dc.contributor.authorLee, JCen_US
dc.contributor.authorLee, HYen_US
dc.contributor.authorNiu, WFen_US
dc.date.accessioned2014-12-08T15:38:26Z-
dc.date.available2014-12-08T15:38:26Z-
dc.date.issued2004-10-01en_US
dc.identifier.issn0094-9655en_US
dc.identifier.urihttp://dx.doi.org/10.1080/00949650310001643270en_US
dc.identifier.urihttp://hdl.handle.net/11536/26320-
dc.description.abstractWe propose an estimation procedure for time-series regression models under the Bayesian inference framework. With the exact method of Wise [Wise, J. (1955). The autocorrelation function and spectral density function. Biometrika, 42, 151-159], an exact likelihood function can be obtained instead of the likelihood conditional on initial observations. The constraints on the parameter space arising from the stationarity conditions are handled by a reparametrization, which was not taken into consideration by Chib [Chib, S. (1993). Bayes regression with autoregressive errors: A Gibbs sampling approach. J. Econometrics, 58, 275-294] or Chib and Greenberg [Chib, S. and Greenberg, E. (1994). Bayes inference in regression model with ARMA(p, q) errors. J. Econometrics, 64, 183-206]. Simulation studies show that our method leads to better inferential results than their results.en_US
dc.language.isoen_USen_US
dc.subjectautoregressive processen_US
dc.subjectexact likelihooden_US
dc.subjectMarkov chain Monte Carloen_US
dc.subjectpartial autocorrelationsen_US
dc.titleBayesian estimation for time-series regressions improved with exact likelihoodsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00949650310001643270en_US
dc.identifier.journalJOURNAL OF STATISTICAL COMPUTATION AND SIMULATIONen_US
dc.citation.volume74en_US
dc.citation.issue10en_US
dc.citation.spage727en_US
dc.citation.epage740en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000221998900002-
dc.citation.woscount0-
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