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A Composite Likelihood Approach for Dynamic Structural Models
We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.
Approximating Time Varying Structural Models With Time Invariant Structures
The paper studies how parameter variation affects the decision rules of a DSGE model and structural inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. Identifi cation and inferential distortions when a constant parameter model is incorrectly assumed are examined. Likelihood and VAR-based estimates of the structural dynamics when parameter variations are neglected are compared. Time variations in the financial frictions of Gertler and Karadi's (2010) model are studied.
Monetary disturbances matter for business fluctuations in the G-7
This paper examines the importance of monetary disturbances for cyclical fluctuations in real activity and inflation. It employs a novel identification approach which uses the sign of the cross-correlation function in response to shocks to assign a structural interpretation to orthogonal innovations. We find that monetary shocks significantly drive output and inflation cycles in all G-7 countries; that they are the dominant source of fluctuations in three of the seven countries; that they contain an important policy component, and that their impact is time varying.