Abstract
The DSGE-VAR model consists of two models of vector autoregressions: the first one approximates the linearised solution of the dynamic stochastic general equilibrium model and is used as a tool for construction of a prior distribution for the second one, estimated with the observed data. The main purpose of the paper is to present practical aspects of DSGE-VAR estimation, verification and comparison, based on the marginal data density. It can be obtained after considering conditional models or by estimation of fully specified models, after assuming uniform, generalised gamma and modified beta distributions. The conditional models lead to serious variability of the Bayes factors that has little economic interpretation. Posterior inference for the weighting parameter from fully estimated models is less sensitive to its prior specification. In the second part of the paper author discusses convergence diagnostics used for checking stability of MCMC algorithms.
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