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dc.contributor.authorCoenen, Günteren
dc.contributor.authorChristoffel, Kaien
dc.contributor.authorWarne, Andersen
dc.date.accessioned2010-08-11T08:56:05Z-
dc.date.available2010-08-11T08:56:05Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/10419/37455-
dc.description.abstractIn this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus on the estimation of their predictive distributions, with special attention being paid to the mean and the covariance matrix of h-steps ahead forecasts. In the empirical analysis, we examine the forecasting performance of the New Area-Wide Model (NAWM) that has been designed for use in the macroeconomic projections at the European Central Bank. The forecast sample covers the period following the introduction of the euro and the out-of-sample performance of the NAWM is compared to nonstructural benchmarks, such as Bayesian vector autoregressions (BVARs). Overall, the empirical evidence indicates that the NAWM compares quite well with the reduced-form models and the results are therefore in line with previous studies. Yet there is scope for improving the NAWM's forecasting performance. For example, the model is not able to explain the moderation in wage growth over the forecast evaluation period and, therefore, it tends to overestimate nominal wages. As a consequence, both the multivariate point and density forecasts using the log determinant and the log predictive score, respectively, suggest that a large BVAR can outperform the NAWM.en
dc.language.isoengen
dc.publisher|aVerein für Socialpolitik |cFrankfurt a. M.en
dc.relation.ispartofseries|aBeiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Forecasting Methods |xA11-V1en
dc.subject.jelC11en
dc.subject.jelC32en
dc.subject.jelE32en
dc.subject.ddc330en
dc.subject.keywordBayesian inferenceen
dc.subject.keywordDSGE modelsen
dc.subject.keywordeuro areaen
dc.subject.keywordforecastingen
dc.subject.keywordopen-economy macroeconomicsen
dc.subject.keywordvector autoregressionen
dc.titleForecasting with DSGE Models-
dc.typeConference Paperen
dc.identifier.ppn655930221en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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