Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be...

A method to estimate DSGE models using the raw data is proposed. The approach links the observables to the model counterparts via a flexible specification which does not require the model-based component to be located solely at business cycle frequencies, allows the non-model-based component to...

We investigate identification issues in DSGE models and their consequences for parameter estimation and model evaluation when the objective function measures the distance between estimated and model-based impulse responses. Observational equivalence, partial and weak identification problems are...

A method to evaluate cyclical models not requiring knowledge of the DGP and the exact specification of the aggregate decision rules is proposed. We derive robust restrictions in a class of models; use some to identify structural shocks in the data and others to evaluate the class or contrast...