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Article: On the quasi-likelihood estimation for random coefficient autoregressions

TitleOn the quasi-likelihood estimation for random coefficient autoregressions
Authors
KeywordsInteger-valued time series
Quasi-likelihood estimation
Random coefficient autoregressions
Issue Date2012
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/02331888.asp
Citation
Statistics, 2012, v. 46 n. 4, p. 505-521 How to Cite?
AbstractWe examine the Gaussian quasi-maximum likelihood estimator (QMLE) for random coefficient autoregressions. Consistency and asymptotic normality are established for general random coefficients and general correlation structure between these coefficients and the noise. In particular, the obtained results apply even if the stationary solution has infinite absolute mean or infinite variance. Next an application to the integer-valued times series modelling is given which provides a novel alternative for traditional INAR-like models for these series. © 2012 Copyright Taylor and Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/159911
ISSN
2015 Impact Factor: 0.53
2015 SCImago Journal Rankings: 0.664
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTruquet, Len_US
dc.contributor.authorYao, Jen_US
dc.date.accessioned2012-08-16T05:59:12Z-
dc.date.available2012-08-16T05:59:12Z-
dc.date.issued2012en_US
dc.identifier.citationStatistics, 2012, v. 46 n. 4, p. 505-521en_US
dc.identifier.issn0233-1888-
dc.identifier.urihttp://hdl.handle.net/10722/159911-
dc.description.abstractWe examine the Gaussian quasi-maximum likelihood estimator (QMLE) for random coefficient autoregressions. Consistency and asymptotic normality are established for general random coefficients and general correlation structure between these coefficients and the noise. In particular, the obtained results apply even if the stationary solution has infinite absolute mean or infinite variance. Next an application to the integer-valued times series modelling is given which provides a novel alternative for traditional INAR-like models for these series. © 2012 Copyright Taylor and Francis Group, LLC.-
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/02331888.asp-
dc.relation.ispartofStatisticsen_US
dc.rightsThis is an electronic version of an article published in Statistics, 2012, v. 46 n. 4, p. 505-521. The journal article is available online at: http://www.tandfonline.com/doi/abs/10.1080/02331888.2010.541557-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectInteger-valued time series-
dc.subjectQuasi-likelihood estimation-
dc.subjectRandom coefficient autoregressions-
dc.titleOn the quasi-likelihood estimation for random coefficient autoregressionsen_US
dc.typeArticleen_US
dc.identifier.emailYao, J: jeffyao@hku.hken_US
dc.identifier.authorityYao, J=rp01473en_US
dc.description.naturepostprint-
dc.identifier.doi10.1080/02331888.2010.541557-
dc.identifier.scopuseid_2-s2.0-84863816864-
dc.identifier.hkuros205366en_US
dc.identifier.volume46en_US
dc.identifier.issue4-
dc.identifier.spage505en_US
dc.identifier.epage521en_US
dc.identifier.isiWOS:000306461200007-
dc.publisher.placeUnited Kingdom-
dc.customcontrol.immutablejt 130405-

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