hero working papers

On the Statistical Identification of DSGE Models

Number: 324
Year: 2007
Author(s): Agostino Consolo, Carlo A. Favero andAlessia Paccagnini
Dynamic Stochastic General Equilibrium (DSGE) models are now con-
sidered attractive by the profession not only from the theoretical perspec-
tive but also from an empirical standpoint. As a consequence of this
development, methods for diagnosing the fit of these models are being
proposed and implemented. In this article we illustrate how the concept
of statistical identification, that was introduced and used by Spanos(1990)
to criticize traditional evaluation methods of Cowles Commission models,
could be relevant for DSGE models. We conclude that the recently pro-
posed model evaluation method, based on the DSGE - VAR(λ), might not satisfy
the condition for statistical identification. However, our appli-
cation also shows that the adoption of a FAVAR as a statistically identified
benchmark leaves unaltered the support of the data for the DSGE model
and that a DSGE-FAVAR can be an optimal forecasting model.

Keywords: Bayesian analysis; Dynamic stochastic general equilibrium model; Model evaluation, Statistical Identification, Vector autoregression, Factor-Augmented Vector Autoregression
JEL codes: C11, C52