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dc.contributor.authorWichitaksorn, Nen_NZ
dc.date.accessioned2019-02-18T02:57:02Z
dc.date.available2019-02-18T02:57:02Z
dc.date.copyright2018-12-07en_NZ
dc.identifier.citationAvailable at SSRN: https://ssrn.com/abstract=3066981 or http://dx.doi.org/10.2139/ssrn.3066981
dc.identifier.urihttp://hdl.handle.net/10292/12258
dc.description.abstractThis paper proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two covariance matrices allow the extension of MtAR to a structural MtAR analysis. The proposed MtAR can also incorporate different lag orders across VAR systems that provide more flexibility to the model. The estimation results from a simulation study and an empirical study on macroeconomic application show favorable performance of our proposed models and method.
dc.publisherSSRN-Elsevieren_NZ
dc.relation.urihttps://ssrn.com/abstract=3066981
dc.rightsCopyright © 2017 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.subjectMarkov chain Monte Carlo; Multivariate analysis; Matrix-variate normal distribution; Autoregression
dc.titleAnalyzing Multiple Vector Autoregressions Through Matrix-variate Normal Distribution With Two Covariance Matricesen_NZ
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.2139/ssrn.3066981
pubs.elements-id320405


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