Methods to estimate dynamic stochastic general equilibrium models

This paper employs the one-sector real business cycle model as a testing ground for four different procedures to estimate dynamic stochastic general equilibrium (DSGE) models. The procedures are: (1) maximum likelihood, with and without measurement errors and incorporating priors, (2) generalized method of moments, (3) simulated method of moments, and (4) indirect inference. Monte carlo analysis is used to study the small-sample properties of these estimators and to examine the implications of misspecification, stochastic singularity, and weak identification.