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dc.contributor.authorCoenen, Günteren_US
dc.contributor.authorChristoffel, Kaien_US
dc.contributor.authorWarne, Andersen_US
dc.date.accessioned2010-08-11T08:56:05Z-
dc.date.available2010-08-11T08:56:05Z-
dc.date.issued2010en_US
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_US
dc.language.isoengen_US
dc.publisherVerein für Socialpolitik Frankfurt a. M.en_US
dc.relation.ispartofseriesBeiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Forecasting Methods A11-V1en_US
dc.subject.jelC11en_US
dc.subject.jelC32en_US
dc.subject.jelE32en_US
dc.subject.ddc330en_US
dc.subject.keywordBayesian inferenceen_US
dc.subject.keywordDSGE modelsen_US
dc.subject.keywordeuro areaen_US
dc.subject.keywordforecastingen_US
dc.subject.keywordopen-economy macroeconomicsen_US
dc.subject.keywordvector autoregressionen_US
dc.titleForecasting with DSGE Modelsen_US
dc.typeConference Paperen_US
dc.identifier.ppn655930221-
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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