Forecasting with DSGE Models
By K. Christoffel, Guenter Coenen, A. Warne. Social Science Research Network, 2010
114 citations recorded by the source corpus.
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. JEL Classification: C11, C32, E32, E37
Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/604b16573b80fe1d1c136049f41aecb465faecfa
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In Economics and Econometrics
- Fotios Petropoulos
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Citations in the corpus
- How Useful Are Estimated DSGE Model Forecasts for Central Bankers? (2010, unknown): “[6] Christo¤el, K.”
- Forecasting: theory and practice (2020, methodology): “For example, the European Central Bank uses the New Area-Wide Model introduced by Warne et al. (2010) and the Federal Reserve Board has created the Estimated, Dynamic, Optimisation-based model (FRB/EDO) as discussed in Chung et al. (2010).”