Matching Theory and Data : Bayesian Vector Autoregression and Dynamic Stochastic General Equilibrium Models

This paper shows how to identify the structural shocks of a Vector Autoregression(VAR) while at the same time estimating a dynamic stochastic generalequilibrium (DSGE) model that is not assumed to replicate the data generatingprocess. It proposes a framework to estimate the parameters of the VAR modeland the DSGE model jointly: the VAR model is identified by sign restrictionsderived from the DSGE model; the DSGE model is estimated by matching thecorresponding impulse response functions.