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dc.contributor.authorMACIEJOWSKA, Katarzyna
dc.date.accessioned2010-07-02T09:39:02Z
dc.date.available2010-07-02T09:39:02Z
dc.date.issued2010
dc.identifier.issn1725-6704
dc.identifier.urihttp://hdl.handle.net/1814/14235
dc.description.abstractThis paper addresses the issue of obtaining maximum likelihood estimates of parameters for structural VAR models with a mixture of distributions. Hence the problem does not have a closed form solution, numerical optimization procedures need to be used. A Monte Carlo experiment is design to compare the performance of four maximization algorithms and two estimation strategies. It is shown that the EM algorithm outperforms the general maximization algorithms such as BFGS, NEWTON and BHHH. Moreover simplification of the probelm introduced in the two steps quasi ML method does not worsen small sample properties of the estimators and therefore may be recommended in the empirical analysis.en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.relation.ispartofseriesEUI ECOen
dc.relation.ispartofseries2010/27en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectStructural vetcor autoregressionen
dc.subjectError correction modelsen
dc.subjectMixed normalen
dc.subjectMonte Carloen
dc.subjectC32en
dc.subjectC46en
dc.titleEstimation Methods Comparison of SVAR Model with the Mixture of Two Normal Distributions – Monte Carlo Analysisen
dc.typeWorking Paperen
dc.neeo.contributorMACIEJOWSKA|Katarzyna|aut|
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