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.
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