Forecasting with Factor-augmented Error Correction Models

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Show simple item record MASTEN, Igor BANERJEE, Anindya MARCELLINO, Massimiliano 2009-06-25T10:48:48Z 2009-06-25T10:48:48Z 2009
dc.identifier.issn 1028-3625
dc.description.abstract As 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.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries EUI RSCAS en
dc.relation.ispartofseries 2009/32 en
dc.rights info:eu-repo/semantics/openAccess
dc.subject Forecasting en
dc.subject Dynamic Factor Models en
dc.subject Error Correction Models en
dc.subject Cointegration en
dc.subject actor-augmented Error Correction Models en
dc.subject FAVAR en
dc.subject C32 en
dc.subject E17 en
dc.title Forecasting with Factor-augmented Error Correction Models en
dc.type Working Paper en
dc.neeo.contributor MASTEN|Igor|aut|
dc.neeo.contributor BANERJEE|Anindya|aut|
dc.neeo.contributor MARCELLINO|Massimiliano|aut|EUI70008
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