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Bayesian analysis of DSGE models

Listed author(s):
  • Sungbae An
  • Frank Schorfheide

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the nonlinear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models, and a DSGE model that was solved with a second-order perturbation method.

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Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 06-5.

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Date of creation: 2006
Handle: RePEc:fip:fedpwp:06-5
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