Data Cloning Estimation and Identification of a Medium-Scale DSGE Model

We apply the data cloning method to estimate a medium-scale dynamic stochastic general equilibrium model. The algorithm is numerical that employs replicas of original sample approximate maximum likelihood estimator as limit Bayesian simulation-based estimators. also analyze identification properties measure individual strength each parameter by observing posterior volatility estimates and access problem globally through eigenvalue covariance matrix. Our results corroborate existing evidence suggesting DSGE model Smeets Wouters only poorly identified. displays weak global properties, many its parameters seem locally ill-identified.