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Multivariate Forecasting with BVARs and DSGE Models

Tim Berg ()

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper I assess the ability of Bayesian vector autoregressions (BVARs) and dynamic stochastic general equilibrium (DSGE) models of different size to forecast comovements of major macroeconomic series in the euro area. Both approaches are compared to unrestricted VARs in terms of multivariate point and density forecast accuracy measures as well as event probabilities. The evidence suggests that BVARs and DSGE models produce accurate multivariate forecasts even for larger datasets. I also detect that BVARs are well calibrated for most events, while DSGE models are poorly calibrated for some. In sum, I conclude that both are useful tools to achieve parameter dimension reduction.

Keywords: BVARs; DSGE Models; Multivariate Forecasting; Large Dataset; Simulation Methods; Euro Area (search for similar items in EconPapers)
JEL-codes: C11 C52 C53 C55 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-eec, nep-ets, nep-for, nep-mac, nep-mfd and nep-ore
Date: 2015-02-24
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https://mpra.ub.uni-muenchen.de/62405/1/MPRA_paper_62405.pdf original version (application/pdf)

Related works:
Journal Article: Multivariate Forecasting with BVARs and DSGE Models (2016) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:62405

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