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 Autore- gression (VAR) while at the same time estimating a dynamic stochastic general equilibrium (DSGE) model that is not assumed to replicate the data generating process. It proposes a framework to estimate the parameters of the VAR model and the DSGE model jointly: the VAR model is identified by sign restrictions derived from the DSGE model; the DSGE model is estimated by matching the corresponding impulse response functions.