We propose a set of tools for the efficient and robust Bayesian estimation of medium- and large-scale DSGE models while accounting for the effective lower bound on nominal interest rates. We combine a novel nonlinear recursive filter with a computationally efficient piece-wise linear solution...

Using a nonlinear Bayesian likelihood approach that fully accounts for the lower bound on nominal interest rates, we analyze US post-crisis macroeconomic dynamics and provide reference parameter estimates. We find that despite the attention received in the literature, neither the inclusion of...

We propose a new approach for the efficient and robust Bayesian estimation of medium- and large-scale DSGE models with occasionally binding constraints. At its core lies the Ensemble Kalman filter, a novel nonlinear recursive filter, which allows for fast likelihood approximations even for...

Using a nonlinear Bayesian likelihood approach that fully accounts for the zero lower bound on nominal interest rates, the authors analyze US post-crisis business cycle dynamics and provide reference parameter estimates. They find that neither the inclusion of financial frictions nor that of...

Using a nonlinear Bayesian likelihood approach that fully accounts for the lower bound on nominal interest rates, we analyze US post-crisis macroeconomic dynamics and provide reference parameter estimates. We find that despite the attention received in the literature, neither the inclusion of...

This paper studies the macroeconomic effects of central bank forward guidance when central bank credibility is endogenous. In particular, we take a stylized New Keynesian model with an occasionally binding zero lower bound constraint on nominal interest rates and heterogeneous and boundedly...