Show simple item record

dc.contributor.authorMASTEN, Igor
dc.contributor.authorBANERJEE, Anindya
dc.contributor.authorMARCELLINO, Massimiliano
dc.date.accessioned2009-06-25T10:48:48Z
dc.date.available2009-06-25T10:48:48Z
dc.date.issued2009
dc.identifier.issn1028-3625
dc.identifier.urihttp://hdl.handle.net/1814/11765
dc.description.abstractAs a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.relation.ispartofseriesEUI RSCASen
dc.relation.ispartofseries2009/32en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectForecastingen
dc.subjectDynamic Factor Modelsen
dc.subjectError Correction Modelsen
dc.subjectCointegrationen
dc.subjectactor-augmented Error Correction Modelsen
dc.subjectFAVARen
dc.subjectC32en
dc.subjectE17en
dc.titleForecasting with Factor-augmented Error Correction Modelsen
dc.typeWorking Paperen
dc.neeo.contributorMASTEN|Igor|aut|
dc.neeo.contributorBANERJEE|Anindya|aut|
dc.neeo.contributorMARCELLINO|Massimiliano|aut|EUI70008
eui.subscribe.skiptrue


Files in this item

Icon

This item appears in the following Collection(s)

Show simple item record