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Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
ECB Working Paper No. 1185
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
In 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-step 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.
Schlagwörter: 
Bayesian inference
DSGE Models
euro area
forecasting
open-economy macroeconomics
Vector autoregression
JEL: 
C11
C32
E32
E37
Dokumentart: 
Working Paper

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