A composite likelihood approach for dynamic structural models
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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 and formally justiﬁes existing practices. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.