A COMPOSITE LIKELIHOOD APPROACH FOR DYNAMIC STRUCTURAL MODELS

We explain how to use the composite likelihood function to ameliorate estimation, computational and inferential problems in dynamic stochastic general equilibrium models. We combine the information present in different models or data sets to estimate the parameters common across models. We provide intuition for why the methodology works and alternative interpretations of the estimators we construct and of the statistics we employ. We present a number of situations where the methodology has the potential to resolve well-known problems and to provide a justification for existing practices that pool different estimates. In each case, we provide an example to illustrate how the approach works and its properties in practice.

Automated summary

In this article, we discuss the use of composite likelihood function in dynamic stochastic general equilibrium models to address issues related to estimation, computation, and inference. By combining information from different models or datasets, we are able to estimate common parameters and provide alternative interpretations for the methodology used. Various examples are presented to demonstrate the potential of this approach in resolving well-known problems and justifying the pooling of different estimates.